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Linköping University
1.
Nyström, Axel.
Evaluation of Multiple Object Tracking in Surveillance Video.
Degree: Computer Vision, 2019, Linköping University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157666
► Multiple object tracking is the process of assigning unique and consistent identities to objects throughout a video sequence. A popular approach to multiple object…
(more)
▼ Multiple object tracking is the process of assigning unique and consistent identities to objects throughout a video sequence. A popular approach to multiple object tracking, and object tracking in general, is to use a method called tracking-by-detection. Tracking-by-detection is a two-stage procedure: an object detection algorithm first detects objects in a frame, these objects are then associated with already tracked objects by a tracking algorithm. One of the main concerns of this thesis is to investigate how different object detection algorithms perform on surveillance video supplied by National Forensic Centre. The thesis then goes on to explore how the stand-alone alone performance of the object detection algorithm correlates with overall performance of a tracking-by-detection system. Finally, the thesis investigates how the use of visual descriptors in the tracking stage of a tracking-by-detection system effects performance. Results presented in this thesis suggest that the capacity of the object detection algorithm is highly indicative of the overall performance of the tracking-by-detection system. Further, this thesis also shows how the use of visual descriptors in the tracking stage can reduce the number of identity switches and thereby increase performance of the whole system.
Subjects/Keywords: Multiple Object Tracking; Tracking-by-Detection; Object Detection; Object Tracking; Deep Learning; Signal Processing; Signalbehandling
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APA (6th Edition):
Nyström, A. (2019). Evaluation of Multiple Object Tracking in Surveillance Video. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157666
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Nyström, Axel. “Evaluation of Multiple Object Tracking in Surveillance Video.” 2019. Thesis, Linköping University. Accessed March 08, 2021.
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157666.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Nyström, Axel. “Evaluation of Multiple Object Tracking in Surveillance Video.” 2019. Web. 08 Mar 2021.
Vancouver:
Nyström A. Evaluation of Multiple Object Tracking in Surveillance Video. [Internet] [Thesis]. Linköping University; 2019. [cited 2021 Mar 08].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157666.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Nyström A. Evaluation of Multiple Object Tracking in Surveillance Video. [Thesis]. Linköping University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157666
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Adelaide
2.
Wang, Xinyu.
High-performance Object Detection and Tracking Using Deep Learning.
Degree: 2019, University of Adelaide
URL: http://hdl.handle.net/2440/124521
► Human detection and tracking are two fundamental problems in computer vision, which have been cornerstones for many real-world applications such as video surveillance, intelligent transportation…
(more)
▼ Human detection and
tracking are two fundamental problems in computer vision, which have been cornerstones for many real-world applications such as video surveillance, intelligent transportation systems and autonomous driving. Benefiting from deep learning technologies such as convolutional neural networks, modern
object detectors and trackers have been achieving much improved accuracy on public benchmarks. In this work, we aim to improve deep learning based human detection. Our main idea is to exploit semantic context information for human detection by using deeply learned semantic features provided by semantic segmentation masks. These segmentation masks play as an attention mechanism and enforce the detectors to focus on the image regions where potential
object candidates are likely to appear. Furthermore, after reviewing some widely used detection benchmarks, we found that the annotation quality for small and crowd objects does not meet to a satisfied standard. Hence, we introduce a new dataset which includes more than 8000 images for detecting small and crowd targets in fixed angle videos. Meanwhile, a baseline detector was proposed to exploit motion channel features for boosting the detection performance. The experimental results show that our proposed approach significantly improve the detection accuracy for the baseline detectors. In addition to a novel method for
object tracking, we propose to transfer the deep feature which is learned originally for image classification to the visual
tracking domain. The domain adaptation is achieved via some “grafted” auxiliary networks which are trained by regressing the
object location in
tracking frames. Moreover, the adaptation is also naturally used for introducing the objectness concept into visual
tracking. This removes a long-standing target ambiguity in visual
tracking tasks and we illustrate the empirical superiority of the more well-defined task. We also experimentally demonstrate the effectiveness of our proposed tracker on two widely used benchmarks
Advisors/Committee Members: Shen, Chunhua (advisor), Liu, Lingqiao (advisor), School of Computer Science (school).
Subjects/Keywords: Object detection; object tracking; deep learning
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APA (6th Edition):
Wang, X. (2019). High-performance Object Detection and Tracking Using Deep Learning. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/124521
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Wang, Xinyu. “High-performance Object Detection and Tracking Using Deep Learning.” 2019. Thesis, University of Adelaide. Accessed March 08, 2021.
http://hdl.handle.net/2440/124521.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Wang, Xinyu. “High-performance Object Detection and Tracking Using Deep Learning.” 2019. Web. 08 Mar 2021.
Vancouver:
Wang X. High-performance Object Detection and Tracking Using Deep Learning. [Internet] [Thesis]. University of Adelaide; 2019. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/2440/124521.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Wang X. High-performance Object Detection and Tracking Using Deep Learning. [Thesis]. University of Adelaide; 2019. Available from: http://hdl.handle.net/2440/124521
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Australian National University
3.
Zhu, Gao.
An Exploration into Model-Free Online Visual Object Tracking
.
Degree: 2016, Australian National University
URL: http://hdl.handle.net/1885/112128
► This thesis presents a thorough investigation of model-free visual object tracking, a fundamental computer vision task that is essential for practical video analytics applications. Given…
(more)
▼ This thesis presents a thorough investigation of model-free
visual object tracking, a fundamental computer vision task that
is essential for practical video analytics applications. Given
the states of the object in the rst frame, e.g., the position and
size of the target, the computational methods developed and
advanced in this thesis aim at determining target states in
consecutive video frames automatically. In contrast to the
tracking schemes that depend strictly on specic object detectors,
model-free tracking provides conveniently flexible and
competently general solutions where object representations are
initiated in the first frame and adapted in an online manner at
each frame.
We first articulate our motivations and intuitions in Chapter 1,
formulate model-free online visual tracking, illustrate outcomes
on two representative object tracking applications; drone control
and sports video broadcasting analysis, and elaborate other
relevant problems.
In Chapter 2, we review various tracking methodologies employed
by state-ofthe-art trackers and further review related background
knowledge, including several important dataset benchmarks and
workshop challenges, which are widely used for evaluating the
performance of trackers, as well as commonly applied evaluation
protocols in this chapter.
In Chapter 3 through Chapter 6, we then explore the model-free
online visual tracking problem in four different dimensions: 1)
learning a more discriminative classier with a two-layer
classication hierarchy and background contextual clusters; 2)
overcoming the limit of conventionally used local-search scheme
with a global object tracking framework based on instance-specic
object proposals; 3) tracking object affine motion with a
Structured Support Vector Machine (SSVM) framework incorporated
with motion manifold structure; 4) an efficient multiple object
model-free online tracking approach based on a shared pool of
object proposals.
Lastly, as a conclusion and future work outlook, we highlight and
summarize the contribution of this thesis and discuss several
promising research directions in Chapter 7, based on latest work
and their drawbacks of current state-of-the-art trackers.
Subjects/Keywords: object tracking;
model-free;
online
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhu, G. (2016). An Exploration into Model-Free Online Visual Object Tracking
. (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/112128
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Zhu, Gao. “An Exploration into Model-Free Online Visual Object Tracking
.” 2016. Thesis, Australian National University. Accessed March 08, 2021.
http://hdl.handle.net/1885/112128.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Zhu, Gao. “An Exploration into Model-Free Online Visual Object Tracking
.” 2016. Web. 08 Mar 2021.
Vancouver:
Zhu G. An Exploration into Model-Free Online Visual Object Tracking
. [Internet] [Thesis]. Australian National University; 2016. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/1885/112128.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Zhu G. An Exploration into Model-Free Online Visual Object Tracking
. [Thesis]. Australian National University; 2016. Available from: http://hdl.handle.net/1885/112128
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Windsor
4.
Kiani, Kasra.
Estimation, Detection and Tracking of Point Objects ON VIDEO.
Degree: MA, Electrical and Computer Engineering, 2020, University of Windsor
URL: https://scholar.uwindsor.ca/etd/8378
► Detecting an extremely small object in a image has always been an important problem. The problem of detecting an object with circular Point Spread Function…
(more)
▼ Detecting an extremely small
object in a image has always been an important problem. The problem of detecting an
object with circular Point Spread Function (PSF) in a focal plane array (FPA) obtained by imaging sensors has several engineering applications. In a recent work, the maximum likelihood (ML) detector was derived for image observations that were corrupted by Gaussian noise in each pixel. The proposed ML detector is optimal under the assumption that the FPA contains a circular
object that has its signal intensity spread in multiple image pixels in the form of a Gaussian point spread function (PSF) with known standard deviation. The efficiency of estimation is validated by comparing it with Cramér Rao Lower Bound (CRLB). In this thesis, we develop an approach to estimate the PSF's covariance, noise covariance, and total energy of the signal. In this thesis, we generalize these results to a generic (elliptical) PSF.
We applied the proposed method on a real-world application, eye
tracking. Eye
tracking is emerging as an attractive method of human computer interaction. In the last project included in this thesis, we consider the problem of eye gaze detection based on embedded cameras such as webcams. Unlike infrared cameras, the performance of a conventional camera suffers due to fluctuations in ambient light. We developed a novel approach to improve performance. Further, we implemented our proposed ML approach to detect the center of the iris and showed it to be superior to existing approaches. Using these approaches, we demonstrate an eye gaze estimation approach using the embedded webcam of a laptop.
Advisors/Committee Members: Behnam Shahrrava, Balakumar Balasingam.
Subjects/Keywords: Eye Tracking; Eye-Gaze Estimation; Image Processing; Object Detection; Tracking Object
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APA (6th Edition):
Kiani, K. (2020). Estimation, Detection and Tracking of Point Objects ON VIDEO. (Masters Thesis). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/8378
Chicago Manual of Style (16th Edition):
Kiani, Kasra. “Estimation, Detection and Tracking of Point Objects ON VIDEO.” 2020. Masters Thesis, University of Windsor. Accessed March 08, 2021.
https://scholar.uwindsor.ca/etd/8378.
MLA Handbook (7th Edition):
Kiani, Kasra. “Estimation, Detection and Tracking of Point Objects ON VIDEO.” 2020. Web. 08 Mar 2021.
Vancouver:
Kiani K. Estimation, Detection and Tracking of Point Objects ON VIDEO. [Internet] [Masters thesis]. University of Windsor; 2020. [cited 2021 Mar 08].
Available from: https://scholar.uwindsor.ca/etd/8378.
Council of Science Editors:
Kiani K. Estimation, Detection and Tracking of Point Objects ON VIDEO. [Masters Thesis]. University of Windsor; 2020. Available from: https://scholar.uwindsor.ca/etd/8378

Universiteit Utrecht
5.
Deiman, J.
Automatic Tracking, Segmentation, Removal and Replay of Traffic in Panoramic 360-Degree Videos for Virtual Reality.
Degree: 2016, Universiteit Utrecht
URL: http://dspace.library.uu.nl:8080/handle/1874/334268
► The Netherlands Aerospace Centre (NLR) has a virtual reality setup which allows users to experience an airplane fly-over with realistically simulated sound. They want to…
(more)
▼ The Netherlands Aerospace Centre (NLR) has a virtual reality setup which allows users to experience an airplane fly-over with realistically simulated sound. They want to move from using static imagery for the surrounding to video. On top of this, they want to be able to have some form of control over the scenario that they have filmed. This thesis describes a method of automatic detection and segmentation of cars in equirectangular panoramic 360-degree videos by first
tracking connected components during an online phase, and doing post processing to enhance the tracks. This will allow the NLR to remove cars from their videos, and replay them at specified times to create custom videos from a single source video. A user study and an objective study have been conducted to determine the quality of the method. The user study has shown that while videos with replayed vehicles are of lower quality than the originals, the custom videos are still acceptable to the majority of users. The objective study shows that the overall accuracy of the method (including replayable cars not used in the user study videos) is not acceptable for a large number of cars. However, as long as there are at least a few usable detections, custom videos can still be produced.
Advisors/Committee Members: Poppe, R., Veltkamp, R., Aalmoes, R., Lania, H..
Subjects/Keywords: object detection; object tracking; object segmentation; inpainting; virtual reality; panoramic video
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Deiman, J. (2016). Automatic Tracking, Segmentation, Removal and Replay of Traffic in Panoramic 360-Degree Videos for Virtual Reality. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/334268
Chicago Manual of Style (16th Edition):
Deiman, J. “Automatic Tracking, Segmentation, Removal and Replay of Traffic in Panoramic 360-Degree Videos for Virtual Reality.” 2016. Masters Thesis, Universiteit Utrecht. Accessed March 08, 2021.
http://dspace.library.uu.nl:8080/handle/1874/334268.
MLA Handbook (7th Edition):
Deiman, J. “Automatic Tracking, Segmentation, Removal and Replay of Traffic in Panoramic 360-Degree Videos for Virtual Reality.” 2016. Web. 08 Mar 2021.
Vancouver:
Deiman J. Automatic Tracking, Segmentation, Removal and Replay of Traffic in Panoramic 360-Degree Videos for Virtual Reality. [Internet] [Masters thesis]. Universiteit Utrecht; 2016. [cited 2021 Mar 08].
Available from: http://dspace.library.uu.nl:8080/handle/1874/334268.
Council of Science Editors:
Deiman J. Automatic Tracking, Segmentation, Removal and Replay of Traffic in Panoramic 360-Degree Videos for Virtual Reality. [Masters Thesis]. Universiteit Utrecht; 2016. Available from: http://dspace.library.uu.nl:8080/handle/1874/334268

University of Washington
6.
Huang, Tsung-Wei.
Automatic Video Analysis for Electronic Monitoring of Fishery Activities.
Degree: PhD, 2019, University of Washington
URL: http://hdl.handle.net/1773/44796
► Recently, automated imagery analysis techniques have drawn increasing attention in fishery science and industry. Compared to traditional human observing and monitoring, automated imagery analysis techniques…
(more)
▼ Recently, automated imagery analysis techniques have drawn increasing attention in fishery science and industry. Compared to traditional human observing and monitoring, automated imagery analysis techniques are more scalable and deployable, and thus have been widely used in recent years for numerous fishery survey tasks, such as abundance estimation, species identification or length measurement. One of the emerging fishery survey tasks which can effectively take advantage of the automated imagery analysis is the electronic monitoring (EM) for fishery activities. The goal of EM is to monitor the fish catching on fishing vessels, either for scientific survey or legal purpose. For example, a fishing vessel may not retain fish catching if the length is below some threshold or exceeding the quota of the vessel for certain species. Therefore, accurate
tracking, counting, measurement and species classification is required in the EM systems. There are, however, challenges from the inspected subjects and operation environments. Deformable objects, noise from the wild sea surface, and dynamic background, make conventional
tracking, segmentation and classification methods unreliable. To overcome the challenges encountered in the electronic monitoring, this dissertation presents an online 3D
tracking and segmentation system for stereo video based monitoring of rail fish catching on wild sea surface. Based on the result of a pre-trained image
object (fish) detector, a Kalman filtering-based
tracking system overcomes the issues of low detection scores of deformed objects and unreliable bounding boxes by rescoring multiple
object proposals using spatial information in 3D. A clustering-and-scoring strategy is then applied on the depth map, so that a plane classification method can effectively segment the objects from the dynamic background without any prior modeling. The
object segmentation is further refined using fully connected conditional random fields based on color and geometric features. With the segmentation results, we can measure the 3D lengths of objects and update the positions of bounding boxes to help
tracking. Experimental results show that a reliable
tracking and measurement performance under noisy and dynamic sea surface environment is achieved. Once fish are tracked and measured, one of the primary tasks in the electronic monitoring is to identify the species of fish. In the work of
object classification, one challenge is that the feature generation needs to be robust with fish in any orientations and poses, which yield diverse visual features and large within-class variation. Other challenges include the high visual similarity among fish species. Therefore, in this dissertation, we utilize the deep metric learning to learn a feature representation which can separate visually similar species in the feature space. By adding more constraints based on the temporal order of image sequences, we can make the model to learn a more structured and compact feature space. Besides, by exploiting the clustering properties and…
Advisors/Committee Members: Hwang, Jenq-Neng (advisor).
Subjects/Keywords: object classification; object segmentation; object tracking; Electrical engineering; Electrical engineering
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❌
APA ·
Chicago ·
MLA ·
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Export
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APA (6th Edition):
Huang, T. (2019). Automatic Video Analysis for Electronic Monitoring of Fishery Activities. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/44796
Chicago Manual of Style (16th Edition):
Huang, Tsung-Wei. “Automatic Video Analysis for Electronic Monitoring of Fishery Activities.” 2019. Doctoral Dissertation, University of Washington. Accessed March 08, 2021.
http://hdl.handle.net/1773/44796.
MLA Handbook (7th Edition):
Huang, Tsung-Wei. “Automatic Video Analysis for Electronic Monitoring of Fishery Activities.” 2019. Web. 08 Mar 2021.
Vancouver:
Huang T. Automatic Video Analysis for Electronic Monitoring of Fishery Activities. [Internet] [Doctoral dissertation]. University of Washington; 2019. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/1773/44796.
Council of Science Editors:
Huang T. Automatic Video Analysis for Electronic Monitoring of Fishery Activities. [Doctoral Dissertation]. University of Washington; 2019. Available from: http://hdl.handle.net/1773/44796

Delft University of Technology
7.
Arya Senna Abdul Rachman, Arya (author).
3D-LIDAR Multi Object Tracking for Autonomous Driving: Multi-target Detection and Tracking under Urban Road Uncertainties.
Degree: 2017, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:f536b829-42ae-41d5-968d-13bbaa4ec736
► The recent advancement of the autonomous vehicle has raised the need for reliable environmental perception. This is evident, as an autonomous vehicle has to perceive…
(more)
▼ The recent advancement of the autonomous vehicle has raised the need for reliable environmental perception. This is evident, as an autonomous vehicle has to perceive and interpret its local environment in order to execute reactive and predictive control action. Object Tracking is an integral part of vehicle perception, as it enables the vehicle to estimate surrounding objects trajectories to achieve dynamic motion planning. The 3D LIDAR has been widely used in object tracking research since the mechanically compact sensor provides rich, far-reaching and real-time data of spatial information around the vehicle. On the other hand, the development of autonomous driving is heading toward its use in the urban-driving situation. In an urban situation, a robust detection and tracking algorithm is required due to increasing number of Vulnerable Road User (e.g. pedestrian and cyclist), heterogeneous terrain, inherent measurement uncertainties and limited sensor reach. This thesis presents an integrated framework of multi-target object detection and tracking using 3D LIDAR geared toward urban use. The framework combines occlusion-aware detection methods, probabilistic adaptive filtering and computationally efficient heuristics logic-based filtering to handle uncertainties arising from sensing limitation of 3D LIDAR and complexity of the target object movement. The implemented framework takes a raw 3D LIDAR data as input to perform multi-target object detection while simultaneously maintaining track of the detected objects' kinematic states and dimension in robust, causal, and real-time manner. Robust detection is enabled by slope-based ground removal and L-shape fitting to reliably enclose bounding box to objects of interest in the presence of sensor occlusion. The tracker utilises three combined Bayesian filters (IMM-UK-JPDAF) which simultaneously tackle association uncertainties, motion uncertainties and estimate non-linear stochastic motion model in real-time. Logic-based rule filters are also designed to augment the rest of detection and tracking based on the understanding of LIDAR sensor limitation and occlusion characteristic. The evaluation results using real-world pre-recorded 3D LIDAR data show the proposed framework can achieve promising real-time tracking performance in urban situations. Diverse datasets are deliberately chosen to evaluate if the MOT system is capable of working in a varying driving scenario. The benchmark results highlight that the designed and implemented MOT system is performing on par with the state-of-art works and yield satisfying accuracy and precision in most given urban settings.
Systems and Control
Advisors/Committee Members: Campoy Cervera, Pascual (mentor), Sefati, Mohsen (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Multi-object Tracking; Vehicle Perception; 3D LIDAR; Object detection; Object tracking; Probabilistic Tracking; Autonomous driving; Machine vision; Adaptive Filtering; Heuristic Rules
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
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APA (6th Edition):
Arya Senna Abdul Rachman, A. (. (2017). 3D-LIDAR Multi Object Tracking for Autonomous Driving: Multi-target Detection and Tracking under Urban Road Uncertainties. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f536b829-42ae-41d5-968d-13bbaa4ec736
Chicago Manual of Style (16th Edition):
Arya Senna Abdul Rachman, Arya (author). “3D-LIDAR Multi Object Tracking for Autonomous Driving: Multi-target Detection and Tracking under Urban Road Uncertainties.” 2017. Masters Thesis, Delft University of Technology. Accessed March 08, 2021.
http://resolver.tudelft.nl/uuid:f536b829-42ae-41d5-968d-13bbaa4ec736.
MLA Handbook (7th Edition):
Arya Senna Abdul Rachman, Arya (author). “3D-LIDAR Multi Object Tracking for Autonomous Driving: Multi-target Detection and Tracking under Urban Road Uncertainties.” 2017. Web. 08 Mar 2021.
Vancouver:
Arya Senna Abdul Rachman A(. 3D-LIDAR Multi Object Tracking for Autonomous Driving: Multi-target Detection and Tracking under Urban Road Uncertainties. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Mar 08].
Available from: http://resolver.tudelft.nl/uuid:f536b829-42ae-41d5-968d-13bbaa4ec736.
Council of Science Editors:
Arya Senna Abdul Rachman A(. 3D-LIDAR Multi Object Tracking for Autonomous Driving: Multi-target Detection and Tracking under Urban Road Uncertainties. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:f536b829-42ae-41d5-968d-13bbaa4ec736

University of Toledo
8.
Ahmadi, Kaveh, ahmadi.
Dim Object Tracking in Cluttered Image Sequences.
Degree: PhD, Engineering, 2016, University of Toledo
URL: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470147209
► This research is aimed at developing efficient dim object tracking techniques in cluttered image sequences. In this dissertation, a number of new techniques are presented…
(more)
▼ This research is aimed at developing efficient dim
object tracking techniques in cluttered image sequences. In this
dissertation, a number of new techniques are presented for image
enhancement, super resolution (SR), dim
object tracking, and
multi-sensor
object tracking. Cluttered images are impaired by
noise. To deal with a mixed Gaussian and impulse noise in the
image, a novel sparse coding super resolution is developed. The
sparse coding has recently become a widely used tool in signal and
image processing. The sparse linear combination of elements from an
appropriately chosen over-complete dictionary can represent many
signal patches. The proposed SR is composed of a Genetic Algorithm
(GA) search step to find the optimum match from low resolution
dictionary. By using GA, the proposed SR is capable of efficiently
up-sampling the low resolution images while preserving the image
details.Dim
object tracking in a heavy clutter environment is a
theoretical and technological challenge in the field of image
processing. For a small dim
object, conventional
tracking methods
fail for the lack of geometrical information. Multiple Hypotheses
Testing (MHT) is one of the generally accepted methods in target
tracking systems. However, processing a tree structure with a
significant number of branches in MHT has been a challenging issue.
Tracking high-speed objects with traditional MHT requires some
presumptions which limit the capabilities of these methods. In this
dissertation, a hierarchal
tracking system in two levels is
presented to solve this problem. For each point in the lower-level,
a Multi Objective Particle Swarm Optimization (MOPSO) technique is
applied to a group of consecutive frames in order to reduce the
number of branches in each
tracking tree. Thus, an optimum track
for each moving
object is obtained in a group of frames. In the
upper-level, an iterative process is used to connect the matching
optimum tracks of the consecutive frames based on the spatial
information and fitness values. Another problem of dim
object
tracking is background subtraction which is difficult due to noisy
environment. This dissertation presents a novel algorithm for
detecting and
tracking small dim targets in Infrared (IR) image
sequences with low Signal to Noise Ratio (SNR) based on the
frequency and spatial domain information. Using a Dual-Tree Complex
Wavelet Transform (DT-CWT), a Constant False Alarm Rate (CFAR)
detector is applied in the frequency domain to find potential
positions of objects in a frame. Following this step, a Support
Vector Machine (SVM) classification is applied to accept or reject
each potential point based on the spatial domain information of the
frame. The combination of the frequency and spatial domain
information demonstrates the high efficiency and accuracy of the
proposed method which is supported by the experimental results.One
of the important tools applied in this dissertation is Particle
Filter (PF). The PF, a nonparametric implementation of the Bayes
filter, is commonly used to estimate the state of a…
Advisors/Committee Members: Salari, Ezzatollah (Committee Chair).
Subjects/Keywords: Computer Engineering; Computer Science; Dim Object Tracking; Particle Filter; Particle Swarm Optimization; Multiple Object Tracking; Multi Sensor Object Tracking; Super Resolution
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Manager
APA (6th Edition):
Ahmadi, Kaveh, a. (2016). Dim Object Tracking in Cluttered Image Sequences. (Doctoral Dissertation). University of Toledo. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470147209
Chicago Manual of Style (16th Edition):
Ahmadi, Kaveh, ahmadi. “Dim Object Tracking in Cluttered Image Sequences.” 2016. Doctoral Dissertation, University of Toledo. Accessed March 08, 2021.
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470147209.
MLA Handbook (7th Edition):
Ahmadi, Kaveh, ahmadi. “Dim Object Tracking in Cluttered Image Sequences.” 2016. Web. 08 Mar 2021.
Vancouver:
Ahmadi, Kaveh a. Dim Object Tracking in Cluttered Image Sequences. [Internet] [Doctoral dissertation]. University of Toledo; 2016. [cited 2021 Mar 08].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470147209.
Council of Science Editors:
Ahmadi, Kaveh a. Dim Object Tracking in Cluttered Image Sequences. [Doctoral Dissertation]. University of Toledo; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470147209

NSYSU
9.
Hsu, Chiang-Hao.
Application of an Omnidirectional Camera to Detection of Moving Objects in 3D Space.
Degree: Master, Mechanical and Electro-Mechanical Engineering, 2011, NSYSU
URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0829111-231710
► Conventional cameras are usually small in their field of view (FOV) and make the observable region limited. Applications by such a vision system may also…
(more)
▼ Conventional cameras are usually small in their field of view (FOV) and make the observable region limited. Applications by such a vision system may also limit motion capabilities for robots when it comes to
object tracking. Omnidirectional camera has a wide FOV which can obtain environmental data from all directions. In comparison with conventional cameras, the wide FOV of omnidirectional cameras reduces blind regions and improves
tracking ability. In this thesis, we assume an omnidirectional camera is mounted on a moving platform, which travels with planar motion. By applying optical flow and CAMShift algorithm to track an
object which is non-propelled and only subjected to gravity. Then, by parabolic fitting, least-square method and Levenberg-Marquardt method to predict the 3D coordinate of the
object at the current instant and the next instant, we can finally predict the position of the drop point and drive the moving platform to meet the
object at the drop point. The
tracking operation and drop point prediction can be successfully achieved even if the camera is under planar motion and rotation.
Advisors/Committee Members: Cheng, Chi-Cheng (committee member), Perng, Jau-Woei (chair), Her, Innchyn (chair).
Subjects/Keywords: Omnidirectional image; Object tracking; CAMShift; Optical flow
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hsu, C. (2011). Application of an Omnidirectional Camera to Detection of Moving Objects in 3D Space. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0829111-231710
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Hsu, Chiang-Hao. “Application of an Omnidirectional Camera to Detection of Moving Objects in 3D Space.” 2011. Thesis, NSYSU. Accessed March 08, 2021.
http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0829111-231710.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Hsu, Chiang-Hao. “Application of an Omnidirectional Camera to Detection of Moving Objects in 3D Space.” 2011. Web. 08 Mar 2021.
Vancouver:
Hsu C. Application of an Omnidirectional Camera to Detection of Moving Objects in 3D Space. [Internet] [Thesis]. NSYSU; 2011. [cited 2021 Mar 08].
Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0829111-231710.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Hsu C. Application of an Omnidirectional Camera to Detection of Moving Objects in 3D Space. [Thesis]. NSYSU; 2011. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0829111-231710
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
10.
Lopes, Nuno Vieira.
Fuzzy logic based approach for object feature tracking.
Degree: 2012, Instituto Politécnico de Leiria
URL: https://www.rcaap.pt/detail.jsp?id=oai:iconline.ipleiria.pt:10400.8/3259
► This thesis introduces a novel technique for feature tracking in sequences of greyscale images based on fuzzy logic. A versatile and modular methodology for feature…
(more)
▼ This thesis introduces a novel technique for feature tracking in sequences of
greyscale images based on fuzzy logic. A versatile and modular methodology
for feature tracking using fuzzy sets and inference engines is presented.
Moreover, an extension of this methodology to perform the correct tracking
of multiple features is also presented.
To perform feature tracking three membership functions are initially
defined. A membership function related to the distinctive property of the feature
to be tracked. A membership function is related to the fact of considering
that the feature has smooth movement between each image sequence and a
membership function concerns its expected future location. Applying these
functions to the image pixels, the corresponding fuzzy sets are obtained and
then mathematically manipulated to serve as input to an inference engine.
Situations such as occlusion or detection failure of features are overcome
using estimated positions calculated using a motion model and a state vector
of the feature.
This methodology was previously applied to track a single feature identified
by the user. Several performance tests were conducted on sequences of
both synthetic and real images. Experimental results are presented, analysed
and discussed. Although this methodology could be applied directly to multiple
feature tracking, an extension of this methodology has been developed
within that purpose. In this new method, the processing sequence of each
feature is dynamic and hierarchical. Dynamic because this sequence can
change over time and hierarchical because features with higher priority will
be processed first. Thus, the process gives preference to features whose location
are easier to predict compared with features whose knowledge of their
behavior is less predictable. When this priority value becomes too low, the
feature will no longer tracked by the algorithm. To access the performance
of this new approach, sequences of images where several features specified
by the user are to be tracked were used.
In the final part of this work, conclusions drawn from this work as well as
the definition of some guidelines for future research are presented.
Nesta tese é introduzida uma nova técnica de seguimento de pontos característicos de objectos em sequências de imagens em escala de cinzentos baseada em lógica difusa. É apresentada uma metodologia versátil e modular para o seguimento de objectos utilizando conjuntos difusos e motores de inferência. É também apresentada uma extensão desta metodologia para o correcto seguimento de múltiplos pontos característicos.
Para se realizar o seguimento são definidas inicialmente três funções de pertença. Uma função de pertença está relacionada com a propriedade distintiva do objecto que desejamos seguir, outra está relacionada com o facto de se considerar que o objecto tem uma movimentação suave entre cada imagem da sequência e outra função de pertença referente à sua previsível localização futura. Aplicando estas funções de pertença aos píxeis da…
Advisors/Committee Members: Melo-Pinto, Pedro, Couto, Pedro M..
Subjects/Keywords: Fuzzy Logic; Object tracking; Lógica difusa
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lopes, N. V. (2012). Fuzzy logic based approach for object feature tracking. (Thesis). Instituto Politécnico de Leiria. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:iconline.ipleiria.pt:10400.8/3259
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Lopes, Nuno Vieira. “Fuzzy logic based approach for object feature tracking.” 2012. Thesis, Instituto Politécnico de Leiria. Accessed March 08, 2021.
https://www.rcaap.pt/detail.jsp?id=oai:iconline.ipleiria.pt:10400.8/3259.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Lopes, Nuno Vieira. “Fuzzy logic based approach for object feature tracking.” 2012. Web. 08 Mar 2021.
Vancouver:
Lopes NV. Fuzzy logic based approach for object feature tracking. [Internet] [Thesis]. Instituto Politécnico de Leiria; 2012. [cited 2021 Mar 08].
Available from: https://www.rcaap.pt/detail.jsp?id=oai:iconline.ipleiria.pt:10400.8/3259.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Lopes NV. Fuzzy logic based approach for object feature tracking. [Thesis]. Instituto Politécnico de Leiria; 2012. Available from: https://www.rcaap.pt/detail.jsp?id=oai:iconline.ipleiria.pt:10400.8/3259
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Ottawa
11.
Stolle, Jacob.
Experimental Modelling of Debris Dynamics in Tsunami-Like Flow Conditions
.
Degree: 2016, University of Ottawa
URL: http://hdl.handle.net/10393/34959
► Tsunamis are among the most devastating and complex natural disasters, affecting coastal regions worldwide. Tsunami waves are generated through many natural phenomena, such as earthquakes,…
(more)
▼ Tsunamis are among the most devastating and complex natural disasters, affecting coastal regions worldwide. Tsunami waves are generated through many natural phenomena, such as earthquakes, landslides, and volcanic eruptions. The waves travel at high speeds away from the source, potentially affecting multiple countries with very little warning. Over the past several decades, tsunamis such as the 2004 Indian Ocean, the 2010 Chilean, and the 2011 Tohoku Tsunami served as reminders of the potential devastation of these natural disasters, resulting in tragic loss of life and billions of dollars in damages. Forensic engineering field investigations and subsequent analysis of these events have demonstrated that infrastructure in these tsunami-prone regions was not adequately prepared for the extreme forces associated with a tsunami. As a result, there has been an increased research emphasis worldwide on the planning and design of infrastructure located in tsunami-prone areas to be better prepared for such future events.
The present study aims to experimentally investigate and analyze the motion of debris carried by an inundating tsunami flood. One of the previous challenges involved in the evaluation of debris motion during such events was a lack of experimental methods that could non-invasively, quickly and accurately track the motion of debris at high velocities. This study introduces two innovative methods of tracking the debris. The first one used a novel camera-based tracking algorithm, while the second used Bluetooth and Inertial Measurement Unit sensors to track the debris within the inundating tsunami flood. The study outlines, for the first time, the technology and methods involved in the two tracking methods as it used both dry-test and wet-test experiments to evaluate the applicability of these methods in coastal and hydraulic engineering.
This study used these two methods to evaluate the motion of debris from experiments conducted in a new Tsunami Wave Basin commissioned recently at Waseda University (Tokyo, Japan). The study examined the effect of the initial positioning of the debris, particularly focusing on the spreading area of the debris (determining thus their maximum displacement and the spreading angle of the debris). The results showed that an increase in the number of the debris resulted in an increase in the spreading angle of the debris and a decrease in the displacement of the debris. The increased number of debris also added significantly more variation in the final resting position of the debris due to the increased debris-debris collisions. The initial orientation of the debris also affected debris motion, particularly influencing the peak velocity of the debris and the distance from the initial debris resting position to where the peak velocity was observed.
Subjects/Keywords: tsunami;
coastal engineering;
debris;
object tracking
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Stolle, J. (2016). Experimental Modelling of Debris Dynamics in Tsunami-Like Flow Conditions
. (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/34959
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Stolle, Jacob. “Experimental Modelling of Debris Dynamics in Tsunami-Like Flow Conditions
.” 2016. Thesis, University of Ottawa. Accessed March 08, 2021.
http://hdl.handle.net/10393/34959.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Stolle, Jacob. “Experimental Modelling of Debris Dynamics in Tsunami-Like Flow Conditions
.” 2016. Web. 08 Mar 2021.
Vancouver:
Stolle J. Experimental Modelling of Debris Dynamics in Tsunami-Like Flow Conditions
. [Internet] [Thesis]. University of Ottawa; 2016. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/10393/34959.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Stolle J. Experimental Modelling of Debris Dynamics in Tsunami-Like Flow Conditions
. [Thesis]. University of Ottawa; 2016. Available from: http://hdl.handle.net/10393/34959
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Melbourne
12.
Lapierre, Mark David.
Interaction of attention and memory: a working memory model of multiple object tracking.
Degree: 2013, University of Melbourne
URL: http://hdl.handle.net/11343/39744
► To accurately perceive a dynamic environment, the visual system must create mental representations of specific objects and their place in the environment, must quickly and…
(more)
▼ To accurately perceive a dynamic environment, the visual system must create mental representations of specific objects and their place in the environment, must quickly and accurately update those representations as objects move, and must be able to use those representations to direct attention to appropriate objects while ignoring those that are irrelevant. This thesis investigated these processes. Three studies were conducted, each focused on a distinct but interrelated aspect of the interaction of attention and memory processes. The first study used a training experimental design to provide evidence that representations of tracking stimuli are not constrained to a visual hemifield in the manner that deployment of attention seems to be. The second study demonstrated mutual disruption between a tracking and a visual working memory task, suggesting that tracking and visual working memory share resources of both attention and memory. The final study provided evidence to suggest that motion does not contribute to tracking when location is more informative. These results are synthesised with previous models of multiple object tracking, and with a multicomponent model of working memory, to develop a model that is able to account for each of the findings of this thesis.
Subjects/Keywords: attention; memory; multiple object tracking; perception; vision
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lapierre, M. D. (2013). Interaction of attention and memory: a working memory model of multiple object tracking. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/39744
Chicago Manual of Style (16th Edition):
Lapierre, Mark David. “Interaction of attention and memory: a working memory model of multiple object tracking.” 2013. Doctoral Dissertation, University of Melbourne. Accessed March 08, 2021.
http://hdl.handle.net/11343/39744.
MLA Handbook (7th Edition):
Lapierre, Mark David. “Interaction of attention and memory: a working memory model of multiple object tracking.” 2013. Web. 08 Mar 2021.
Vancouver:
Lapierre MD. Interaction of attention and memory: a working memory model of multiple object tracking. [Internet] [Doctoral dissertation]. University of Melbourne; 2013. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/11343/39744.
Council of Science Editors:
Lapierre MD. Interaction of attention and memory: a working memory model of multiple object tracking. [Doctoral Dissertation]. University of Melbourne; 2013. Available from: http://hdl.handle.net/11343/39744

California State University – Sacramento
13.
Wadhwani, Hitesh.
A tool for tracking objects through V1KU, a neural network system.
Degree: MS, Computer Science, 2011, California State University – Sacramento
URL: http://hdl.handle.net/10211.9/924
► The intent of this project is to explore the tracking capabilities of V1KU a neural network system. V1KU is a product by General Vision Company…
(more)
▼ The intent of this project is to explore the
tracking capabilities of V1KU a neural network system. V1KU is a product by General Vision Company that comprises of CogniMem neural network chip for real-time image learning and CogniSight image recognition engine. The board also consists of Micron/Aptina monochrome CMOS sensor for visual input. The board has powerful capability to learn and recognize objects simultaneously within a fraction of a second. Due to this ability an application is developed which uses board???s capabilities to track a learned
object in real-time.
The development of this application has gone through various phases of experiments as during initial development stages the board was quite new and very little support was available. After applying the methodology of trial and error I was able to achieve a real-time
tracking working with this board. The people at General Vision also gave their inputs on how to optimize the code so that
tracking works efficiently. The board has the capabilities to track multiple objects simultaneously, but at this present time the goal is to effectively track a single
object. The new version of the board with casing came out recently which has some mounting space that can be utilized in future to mount servo motors to automate the
tracking process. The output of this application forms a basis for stereoscopic
tracking of various objects in real-time.
Advisors/Committee Members: Faroughi, Nikrouz.
Subjects/Keywords: Image recognition; Artificial intelligence; Object tracking
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Wadhwani, H. (2011). A tool for tracking objects through V1KU, a neural network system. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.9/924
Chicago Manual of Style (16th Edition):
Wadhwani, Hitesh. “A tool for tracking objects through V1KU, a neural network system.” 2011. Masters Thesis, California State University – Sacramento. Accessed March 08, 2021.
http://hdl.handle.net/10211.9/924.
MLA Handbook (7th Edition):
Wadhwani, Hitesh. “A tool for tracking objects through V1KU, a neural network system.” 2011. Web. 08 Mar 2021.
Vancouver:
Wadhwani H. A tool for tracking objects through V1KU, a neural network system. [Internet] [Masters thesis]. California State University – Sacramento; 2011. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/10211.9/924.
Council of Science Editors:
Wadhwani H. A tool for tracking objects through V1KU, a neural network system. [Masters Thesis]. California State University – Sacramento; 2011. Available from: http://hdl.handle.net/10211.9/924
14.
Spruyt, Vincent.
Robust and real-time hand detection and tracking in monocular video.
Degree: 2015, Ghent University
URL: http://hdl.handle.net/1854/LU-5872175
► In recent years, personal computing devices such as laptops, tablets and smartphones have become ubiquitous. Moreover, intelligent sensors are being integrated into many consumer devices…
(more)
▼ In recent years, personal computing devices such as laptops, tablets and smartphones have become ubiquitous. Moreover, intelligent sensors are being integrated into many consumer devices such as eyeglasses, wristwatches and smart televisions. With the advent of touchscreen technology, a new human-computer interaction (HCI) paradigm arose that allows users to interface with their device in an intuitive manner. Using simple gestures, such as swipe or pinch movements, a touchscreen can be used to directly interact with a virtual environment. Nevertheless, touchscreens still form a physical barrier between the virtual interface and the real world.
An increasingly popular field of research that tries to overcome this limitation, is video based gesture recognition, hand detection and hand
tracking. Gesture based interaction allows the user to directly interact with the computer in a natural manner by exploring a virtual reality using nothing but his own body language.
In this dissertation, we investigate how robust hand detection and
tracking can be accomplished under real-time constraints. In the context of human-computer interaction, real-time is defined as both low latency and low complexity, such that a complete video frame can be processed before the next one becomes available. Furthermore, for practical applications, the algorithms should be robust to illumination changes, camera motion, and cluttered backgrounds in the scene. Finally, the system should be able to initialize automatically, and to detect and recover from
tracking failure. We study a wide variety of existing algorithms, and propose significant improvements and novel methods to build a complete detection and
tracking system that meets these requirements.
Hand detection, hand
tracking and hand segmentation are related yet technically different challenges. Whereas detection deals with finding an
object in a static image,
tracking considers temporal information and is used to track the position of an
object over time, throughout a video sequence. Hand segmentation is the task of estimating the hand contour, thereby separating the
object from its background.
Detection of hands in individual video frames allows us to automatically initialize our
tracking algorithm, and to detect and recover from
tracking failure. Human hands are highly articulated objects, consisting of finger parts that are connected with joints. As a result, the appearance of a hand can vary greatly, depending on the assumed hand pose. Traditional detection algorithms often assume that the appearance of the
object of interest can be described using a rigid model and therefore can not be used to robustly detect human hands. Therefore, we developed an algorithm that detects hands by exploiting their articulated nature. Instead of resorting to a template based approach, we probabilistically model the spatial relations between different hand parts, and the centroid of the hand. Detecting hand parts, such as fingertips, is much easier than detecting a complete hand. Based on our model of the…
Advisors/Committee Members: Philips, Wilfried, Ledda, Alessandro.
Subjects/Keywords: Technology and Engineering; object detection; hand tracking; Computer Vision; object tracking; particle filtering; hand detection
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Spruyt, V. (2015). Robust and real-time hand detection and tracking in monocular video. (Thesis). Ghent University. Retrieved from http://hdl.handle.net/1854/LU-5872175
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Spruyt, Vincent. “Robust and real-time hand detection and tracking in monocular video.” 2015. Thesis, Ghent University. Accessed March 08, 2021.
http://hdl.handle.net/1854/LU-5872175.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Spruyt, Vincent. “Robust and real-time hand detection and tracking in monocular video.” 2015. Web. 08 Mar 2021.
Vancouver:
Spruyt V. Robust and real-time hand detection and tracking in monocular video. [Internet] [Thesis]. Ghent University; 2015. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/1854/LU-5872175.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Spruyt V. Robust and real-time hand detection and tracking in monocular video. [Thesis]. Ghent University; 2015. Available from: http://hdl.handle.net/1854/LU-5872175
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Vanderbilt University
15.
St.Clair, Rebecca.
Through space and time: An examination of motion in multiple object tracking.
Degree: PhD, Psychology, 2012, Vanderbilt University
URL: http://hdl.handle.net/1803/11419
► Object correspondence is the process by which the visual system matches the identities of objects to their locations to form stable percepts. Object correspondence is…
(more)
▼ Object correspondence is the process by which the visual system matches the identities of objects to their locations to form stable percepts.
Object correspondence is useful when we watch a sporting event, like basketball, in which the players move. The visual system binds the identities of the players to their new locations to maintain their identities as they move. The visual system could perform this task using only position information by updating the locations of each
object using a proximity rule. Alternatively, the visual system may use motion information to bind the identities to the new locations. For example, motion could be used to predict the future locations of moving objects. One way cognitive psychologists study this process in human participants is with the multiple
object tracking task. In this task, participants track target objects as they move among identical distractors. The work in this dissertation examined whether or not motion is used during multiple
object tracking. In my
tracking display, textured objects were presented and I manipulated the direction of the texture motion relative to the direction of the
object motion. I found that
tracking was impaired when the texture motion conflicted with the
object motion because the mental representations of motion did not match the physical motion of objects in the display. I concluded that the visual system depends on motion representations of targets and distractors to solve the
object correspondence problem during
tracking.
Advisors/Committee Members: Thomas Palmeri (committee member), Amy Needham (committee member), Randolph Blake (committee member), Adriane Seiffert (Committee Chair).
Subjects/Keywords: object correspondence; texture; motion; multiple object tracking; attention
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
St.Clair, R. (2012). Through space and time: An examination of motion in multiple object tracking. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11419
Chicago Manual of Style (16th Edition):
St.Clair, Rebecca. “Through space and time: An examination of motion in multiple object tracking.” 2012. Doctoral Dissertation, Vanderbilt University. Accessed March 08, 2021.
http://hdl.handle.net/1803/11419.
MLA Handbook (7th Edition):
St.Clair, Rebecca. “Through space and time: An examination of motion in multiple object tracking.” 2012. Web. 08 Mar 2021.
Vancouver:
St.Clair R. Through space and time: An examination of motion in multiple object tracking. [Internet] [Doctoral dissertation]. Vanderbilt University; 2012. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/1803/11419.
Council of Science Editors:
St.Clair R. Through space and time: An examination of motion in multiple object tracking. [Doctoral Dissertation]. Vanderbilt University; 2012. Available from: http://hdl.handle.net/1803/11419

KTH
16.
Shilo, Albina.
Detection and tracking of unknown objects on the road based on sparse LiDAR data for heavy duty vehicles.
Degree: Electrical Engineering and Computer Science (EECS), 2018, KTH
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-236083
► Environment perception within autonomous driving aims to provide a comprehensive and accurate model of the surrounding environment based on information from sensors. For the…
(more)
▼ Environment perception within autonomous driving aims to provide a comprehensive and accurate model of the surrounding environment based on information from sensors. For the model to be comprehensive it must provide the kinematic state of surrounding objects. The existing approaches of object detection and tracking (estimation of kinematic state) are developed for dense 3D LiDAR data from a sensor mounted on a car. However, it is a challenge to design a robust detection and tracking algorithm for sparse 3D LiDAR data. Therefore, in this thesis we propose a framework for detection and tracking of unknown objects using sparse VLP-16 LiDAR data which is mounted on a heavy duty vehicle. Experiments reveal that the proposed framework performs well detecting trucks, buses, cars, pedestrians and even smaller objects of a size bigger than 61x41x40 cm. The detection distance range depends on the size of an object such that large objects (trucks and buses) are detected within 25 m while cars and pedestrians within 18 m and 15 m correspondingly. The overall multiple objecttracking accuracy of the framework is 79%.
Miljöperception inom autonom körning syftar till att ge en heltäckande och korrekt modell av den omgivande miljön baserat på information från sensorer. För att modellen ska vara heltäckande måste den ge information om tillstånden hos omgivande objekt. Den befintliga metoden för objektidentifiering och spårning (uppskattning av kinematiskt tillstånd) utvecklas för täta 3D-LIDAR-data från en sensor monterad på en bil. Det är dock en utmaning att designa en robust detektions och spårningsalgoritm för glesa 3D-LIDAR-data. Därför föreslår vi ett ramverk för upptäckt och spårning av okända objekt med hjälp av gles VLP-16-LIDAR-data som är monterat på ett tungt fordon. Experiment visar att det föreslagna ramverket upptäcker lastbilar, bussar, bilar, fotgängare och även mindre objekt om de är större än 61x41x40 cm. Detekteringsavståndet varierar beroende på storleken på ett objekt så att stora objekt (lastbilar och bussar) detekteras inom 25 m medan bilar och fotgängare detekteras inom 18 m respektive 15 m på motsvarande sätt. Ramverkets totala precision för objektspårning är 79%.
Subjects/Keywords: Lidar; arbitrary object detection; object tracking; Robotics; Robotteknik och automation
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APA (6th Edition):
Shilo, A. (2018). Detection and tracking of unknown objects on the road based on sparse LiDAR data for heavy duty vehicles. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-236083
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Shilo, Albina. “Detection and tracking of unknown objects on the road based on sparse LiDAR data for heavy duty vehicles.” 2018. Thesis, KTH. Accessed March 08, 2021.
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-236083.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Shilo, Albina. “Detection and tracking of unknown objects on the road based on sparse LiDAR data for heavy duty vehicles.” 2018. Web. 08 Mar 2021.
Vancouver:
Shilo A. Detection and tracking of unknown objects on the road based on sparse LiDAR data for heavy duty vehicles. [Internet] [Thesis]. KTH; 2018. [cited 2021 Mar 08].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-236083.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Shilo A. Detection and tracking of unknown objects on the road based on sparse LiDAR data for heavy duty vehicles. [Thesis]. KTH; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-236083
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Victoria University of Wellington
17.
Ajmal, Aisha.
Active Shift Attention Based Object Tracking System.
Degree: 2020, Victoria University of Wellington
URL: http://hdl.handle.net/10063/9233
► The human vision system (HVS) collects a huge amount of information and performs a variety of biological mechanisms to select relevant information. Computational models based…
(more)
▼ The human vision system (HVS) collects a huge amount of information and performs a variety of biological mechanisms to select relevant information. Computational models based on these biological mechanisms are used in machine vision to select interesting or salient regions in the images for application in scene analysis,
object detection and
object tracking.
Different
object tracking techniques have been proposed often using complex processing methods. On the other hand, attention-based computational models have shown significant performance advantages in various applications. We hypothesise the integration of a visual attention model with
object tracking can be effective in increasing the performance
by reducing the detection complexity in challenging environments such as illumination change, occlusion, and camera moving.
The overall objective of this thesis is to develop a visual saliency based
object tracker that alternates between targets using a measure of current uncertainty derived from a Kalman filter. This thesis presents the results by showing the effectiveness of the tracker using the mean square error when compared to a tracker without the uncertainty mechanism.
Specific colour spaces can contribute to the identification of salient regions. The investigation is done between the non-uniform red, green and blue (RGB) derived opponencies with the hue, saturation and value (HSV) colour space using video information. The main motivation for this particular comparison is to improve the quality of saliency detection in challenging situations such as lighting changes. Precision-Recall curves are used to compare the colour spaces using pyramidal and non-pyramidal saliency models.
Advisors/Committee Members: Hollitt, Christopher, Al-Sahaf, Harith, Frean, Marcus.
Subjects/Keywords: Saliency; Object detection; Kalman Filter; Computer Vision; Object Tracking
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
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Manager
APA (6th Edition):
Ajmal, A. (2020). Active Shift Attention Based Object Tracking System. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/9233
Chicago Manual of Style (16th Edition):
Ajmal, Aisha. “Active Shift Attention Based Object Tracking System.” 2020. Masters Thesis, Victoria University of Wellington. Accessed March 08, 2021.
http://hdl.handle.net/10063/9233.
MLA Handbook (7th Edition):
Ajmal, Aisha. “Active Shift Attention Based Object Tracking System.” 2020. Web. 08 Mar 2021.
Vancouver:
Ajmal A. Active Shift Attention Based Object Tracking System. [Internet] [Masters thesis]. Victoria University of Wellington; 2020. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/10063/9233.
Council of Science Editors:
Ajmal A. Active Shift Attention Based Object Tracking System. [Masters Thesis]. Victoria University of Wellington; 2020. Available from: http://hdl.handle.net/10063/9233

Virginia Tech
18.
Nguyen, Chuong Hoang.
Features identification and tracking for an autonomous ground vehicle.
Degree: MS, Mechanical Engineering, 2013, Virginia Tech
URL: http://hdl.handle.net/10919/33127
► This thesis attempts to develop features identification and tracking system for an autonomous ground vehicle by focusing on four fundamental tasks: Motion detection, object tracking,…
(more)
▼ This thesis attempts to develop features identification and
tracking system for an autonomous ground vehicle by focusing on four fundamental tasks: Motion detection,
object tracking, scene recognition, and
object detection and recognition. For motion detection, we combined the background subtraction method using the mixture of Gaussian models and the optical flow to highlight any moving objects or new entering objects which stayed still. To increase robustness for
object tracking result, we used the Kalman filter to combine the
tracking method based on the color histogram and the method based on invariant features. For scene recognition, we applied the algorithm Census Transform Histogram (CENTRIST), which is based on Census Transform images of the training data and the Support Vector Machine classifier, to recognize a total of 8 scene categories. Because detecting the horizon is also an important task for many navigation applications, we also performed horizon detection in this thesis. Finally, the deformable parts-based models algorithm was implemented to detect some common objects, such as humans and vehicles. Furthermore, objects were only detected in the area under the horizon to reduce the detecting time and false matching rate.
Advisors/Committee Members: Wicks, Alfred L. (committeechair), Abbott, A. Lynn (committee member), Leonessa, Alexander (committee member).
Subjects/Keywords: Motion detection; object tracking; scene recognition; object detection
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MLA ·
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APA (6th Edition):
Nguyen, C. H. (2013). Features identification and tracking for an autonomous ground vehicle. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/33127
Chicago Manual of Style (16th Edition):
Nguyen, Chuong Hoang. “Features identification and tracking for an autonomous ground vehicle.” 2013. Masters Thesis, Virginia Tech. Accessed March 08, 2021.
http://hdl.handle.net/10919/33127.
MLA Handbook (7th Edition):
Nguyen, Chuong Hoang. “Features identification and tracking for an autonomous ground vehicle.” 2013. Web. 08 Mar 2021.
Vancouver:
Nguyen CH. Features identification and tracking for an autonomous ground vehicle. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/10919/33127.
Council of Science Editors:
Nguyen CH. Features identification and tracking for an autonomous ground vehicle. [Masters Thesis]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/33127

Texas A&M University
19.
Nyman, Tristin.
Object Individuation Using Function in Infancy: An Eye-Tracking Study.
Degree: MS, Psychology, 2018, Texas A&M University
URL: http://hdl.handle.net/1969.1/174426
► The age at which infants are able to individuate between objects on the basis of the functional category to which it belongs has yet to…
(more)
▼ The age at which infants are able to individuate between objects on the basis of the functional category to which it belongs has yet to be determined.
Object individuation depends on a variety of
object characteristics such as function, color, shape, or name, as well as infant characteristics such as age. Recently, research has emphasized the importance of individuation using functional information in infancy. In this study, looking time performance for infants aged 3- to 8 months and 12- to 18- months was evaluated using eye-
tracking technology to assess infants’ abilities to individuate objects based on functional categories. Infants were either given the opportunity to create a functional category (i.e., roller and cutter) by viewing functional examples in the Experimental Condition, or they were not given this opportunity in the Control Condition.
Across both conditions no significant differences were found among looking time during the final phase of the test trials for infants aged 3- to 8- months, but there was a significant difference between the scores for the Experimental Condition (M = 0.4303; SD = .244) and the Control Condition (M = 0.2965; SD = .230) during the second test trial; t(87) = 2.596, p = 0.011, d = 0.278. In addition, there was a significant difference in the scores for Experimental (M=0.4827, SD = 0.268) and Control (M = 0.326, SD = 0.171) Conditions during the third test trial; t(87) = 3.099, p =0.002, d = 0.332. Additionally, there was a significant difference between the percent-to-center looking times for Trial 1 (M= 0.302, SD = 0.196) and Trial 2 (M = 0.430, SD = 0.244); t(52) = -3.896, p < .01, d = -0.540; and Trial 1 and Trial 3 (M = 0.483, SD = 0.268); t(52) = -4.099, p ˂.01, d = -0.568 for infants aged 12- to 18- months. This suggests that infants aged 12- to 18- months, but not 3- to 8- months, are able to use functional information to establish categories and use this functional category information to later individuate objects based on function.
Advisors/Committee Members: Wilcox, Teresa (advisor), Heffer, Robert W (advisor), Riccio, Cynthia (committee member).
Subjects/Keywords: Infant development; object categorization; object individuation; eye-tracking
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Nyman, T. (2018). Object Individuation Using Function in Infancy: An Eye-Tracking Study. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/174426
Chicago Manual of Style (16th Edition):
Nyman, Tristin. “Object Individuation Using Function in Infancy: An Eye-Tracking Study.” 2018. Masters Thesis, Texas A&M University. Accessed March 08, 2021.
http://hdl.handle.net/1969.1/174426.
MLA Handbook (7th Edition):
Nyman, Tristin. “Object Individuation Using Function in Infancy: An Eye-Tracking Study.” 2018. Web. 08 Mar 2021.
Vancouver:
Nyman T. Object Individuation Using Function in Infancy: An Eye-Tracking Study. [Internet] [Masters thesis]. Texas A&M University; 2018. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/1969.1/174426.
Council of Science Editors:
Nyman T. Object Individuation Using Function in Infancy: An Eye-Tracking Study. [Masters Thesis]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/174426

Rochester Institute of Technology
20.
Li, Feng.
Optimizations and applications in head-mounted video-based eye tracking.
Degree: Chester F. Carlson Center for Imaging Science (COS), 2011, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/2939
► Video-based eye tracking techniques have become increasingly attractive in many research fields, such as visual perception and human-computer interface design. The technique primarily relies on…
(more)
▼ Video-based eye
tracking techniques have become increasingly attractive in many research fields, such as visual perception and human-computer interface design. The technique primarily relies on the positional difference between the center of the eye's pupil and the first-surface reflection at the cornea, the corneal reflection (CR). This difference vector is mapped to determine an observer's point of regard (POR). In current head-mounted video-based eye trackers, the systems are limited in several aspects, such as inadequate measurement range and misdetection of eye features (pupil and CR). This research first proposes a new `structured illumination' configuration, using multiple IREDs to illuminate the eye, to ensure that eye positions can still be tracked even during extreme eye movements (up to ±45° horizontally and ±25° vertically). Then eye features are detected by a two-stage processing approach. First, potential CRs and the pupil are isolated based on statistical information in an eye image. Second, genuine CRs are distinguished by a novel CR location prediction technique based on the well-correlated relationship between the offset of the pupil and that of the CR. The optical relationship of the pupil and CR offsets derived in this thesis can be applied to two typical illumination configurations - collimated and near-source ones- in the video-based eye
tracking system. The relationships from the optical derivation and that from an experimental measurement match well.
Two application studies, smooth pursuit dynamics in controlled static (laboratory) and unconstrained vibrating (car) environments were conducted. In the first study, the extended stimuli (color photographs subtending 2° and 17°, respectively) were found to enhance smooth pursuit movements induced by realistic images, and the eye velocity for
tracking a small dot (subtending <0.1°) was saturated at about 64 deg/sec while the saturation velocity occurred at higher velocities for the extended images. The difference in gain due to target size was significant between dot and the two extended stimuli, while no statistical difference existed between the two extended stimuli. In the second study, twovisual stimuli same as in the first study were used. The visual performance was impaired dramatically due to the whole body motion in the car, even in the
tracking of a slowly moving target (2 deg/sec); the eye was found not able to perform a pursuit task as smooth as in the static environment though the unconstrained head motion in the unstable condition was supposed to enhance the visual performance.
Advisors/Committee Members: Pelz, Jeff.
Subjects/Keywords: Eye tracker; Eye tracking; Feature detection; Object tracking; Smooth pursuit
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APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Li, F. (2011). Optimizations and applications in head-mounted video-based eye tracking. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/2939
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Li, Feng. “Optimizations and applications in head-mounted video-based eye tracking.” 2011. Thesis, Rochester Institute of Technology. Accessed March 08, 2021.
https://scholarworks.rit.edu/theses/2939.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Li, Feng. “Optimizations and applications in head-mounted video-based eye tracking.” 2011. Web. 08 Mar 2021.
Vancouver:
Li F. Optimizations and applications in head-mounted video-based eye tracking. [Internet] [Thesis]. Rochester Institute of Technology; 2011. [cited 2021 Mar 08].
Available from: https://scholarworks.rit.edu/theses/2939.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Li F. Optimizations and applications in head-mounted video-based eye tracking. [Thesis]. Rochester Institute of Technology; 2011. Available from: https://scholarworks.rit.edu/theses/2939
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
21.
De Ath, G.
Object tracking in video with part-based tracking by feature sampling.
Degree: PhD, 2019, University of Exeter
URL: http://hdl.handle.net/10871/38781
► Visual tracking of arbitrary objects is an active research topic in computer vision, with applications across multiple disciplines including video surveillance, activity analysis, robot vision,…
(more)
▼ Visual tracking of arbitrary objects is an active research topic in computer vision, with applications across multiple disciplines including video surveillance, activity analysis, robot vision, and human computer interface. Despite great progress having been made in object tracking in recent years, it still remains a challenge to design trackers that can deal with difficult tracking scenarios, such as camera motion, object motion change, occlusion, illumination changes, and object deformation. A promising way of tackling these types of problems is to use a part-based method; one which models and tracks small regions of the object and estimates the location of the object based on the tracked part's positions. These approaches typically model parts of objects with histograms of various hand-crafted features extracted from the region in which the part is located. However, it is unclear how such relatively homogeneous regions should be represented to form an effective part-based tracker. In this thesis we present a part-based tracker that includes a model for object parts that is designed to empirically characterise the underlying colour distribution of an image region, representing it by pairs of randomly selected colour features and counts of how many pixels are similar to each feature. This novel feature representation is used to find probable locations for the part in future frames via a Bhattacharyya Distance-based metric, which is modified to prefer higher quality matches. Sets of candidate patch locations are generated by randomly generating non-shearing affine transformations of the part's previous locations and locally optimising the most likely sets of parts to allow for small intra-frame object deformations. We also present a study of model initialisation in online, model-free tracking and evaluate several techniques for selecting the regions of an image, given a target bounding box most likely to contain an object. The strengths and limitations of the combined tracker are evaluated on the VOT2016 and VOT2018 datasets using their evaluation protocol, which also allows an extensive evaluation of parameter robustness. The presented tracker is ranked first among part-based trackers on the VOT2018 dataset and is particularly robust to changes in object and camera motion, as well as object size changes.
Subjects/Keywords: 004; Object Tracking; Computer Vision; Segmentation; Initialisation; Single-Target Tracking
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
De Ath, G. (2019). Object tracking in video with part-based tracking by feature sampling. (Doctoral Dissertation). University of Exeter. Retrieved from http://hdl.handle.net/10871/38781
Chicago Manual of Style (16th Edition):
De Ath, G. “Object tracking in video with part-based tracking by feature sampling.” 2019. Doctoral Dissertation, University of Exeter. Accessed March 08, 2021.
http://hdl.handle.net/10871/38781.
MLA Handbook (7th Edition):
De Ath, G. “Object tracking in video with part-based tracking by feature sampling.” 2019. Web. 08 Mar 2021.
Vancouver:
De Ath G. Object tracking in video with part-based tracking by feature sampling. [Internet] [Doctoral dissertation]. University of Exeter; 2019. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/10871/38781.
Council of Science Editors:
De Ath G. Object tracking in video with part-based tracking by feature sampling. [Doctoral Dissertation]. University of Exeter; 2019. Available from: http://hdl.handle.net/10871/38781

Universidade Nova
22.
Silva, João Miguel Ferreira da.
People and object tracking for video annotation.
Degree: 2012, Universidade Nova
URL: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/8953
► Dissertação para obtenção do Grau de Mestre em Engenharia Informática
Object tracking is a thoroughly researched problem, with a body of associated literature dating at…
(more)
▼ Dissertação para obtenção do Grau de Mestre em
Engenharia Informática
Object tracking is a thoroughly researched problem, with a body of associated literature
dating at least as far back as the late 1970s. However, and despite the development of some satisfactory real-time trackers, it has not yet seen widespread use. This is not due to a lack of applications for the technology, since several interesting ones exist. In this document, it is postulated that this status quo is due, at least in part, to a lack of easy to use software libraries supporting object tracking. An overview of the problems associated with object tracking is presented and the process of developing one such library is documented. This discussion includes how to overcome problems like heterogeneities in
object representations and requirements for training or initial object position hints.
Video annotation is the process of associating data with a video’s content. Associating data with a video has numerous applications, ranging from making large video archives or long videos searchable, to enabling discussion about and augmentation of the video’s content. Object tracking is presented as a valid approach to both automatic and manual video annotation, and the integration of the developed object tracking library into an existing video annotator, running on a tablet computer, is described. The challenges involved in designing an interface to support the association of video annotations with tracked objects in real-time are also discussed. In particular, we discuss our interaction approaches to handle moving object selection on live video, which we have called “Hold and Overlay” and “Hold and Speed Up”. In addition, the results of a set of preliminary tests are reported.
project “TKB – A Transmedia Knowledge Base
for contemporary dance” (PTDC/EA /AVP/098220/2008 funded by FCT/MCTES), the
UTAustin – Portugal, Digital Media Program (SFRH/BD/42662/2007 FCT/MCTES) and by CITI/DI/FCT/UNL (Pest-OE/EEI/UI0527/2011)
Advisors/Committee Members: Correia, Nuno.
Subjects/Keywords: Multimedia; People tracking; Object tracking; Video; Video annotation; Computer vision
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APA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Silva, J. M. F. d. (2012). People and object tracking for video annotation. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/8953
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Silva, João Miguel Ferreira da. “People and object tracking for video annotation.” 2012. Thesis, Universidade Nova. Accessed March 08, 2021.
http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/8953.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Silva, João Miguel Ferreira da. “People and object tracking for video annotation.” 2012. Web. 08 Mar 2021.
Vancouver:
Silva JMFd. People and object tracking for video annotation. [Internet] [Thesis]. Universidade Nova; 2012. [cited 2021 Mar 08].
Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/8953.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Silva JMFd. People and object tracking for video annotation. [Thesis]. Universidade Nova; 2012. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/8953
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Western Ontario
23.
Ramin, Marjan.
Improvements to Tracking Pedestrians in Video Streams Using a Pre-trained Convolutional Neural Network.
Degree: 2016, University of Western Ontario
URL: https://ir.lib.uwo.ca/etd/3886
► Safety has been a very crucial aspect in our lives and much attention has been paid to this issue since we need to remain safe…
(more)
▼ Safety has been a very crucial aspect in our lives and much attention has been paid to this issue since we need to remain safe everywhere.
In 2010, about 270,000 pedestrians were killed on the roads globally, which shows the importance of investigation of different approaches to reduce traffic fatalities.
One way to decrease the number of car accidents with pedestrians is to equip vehicles with cameras that detect and track pedestrians in the road. Many applications have been presented to improve the performance of pedestrian tracking.
However, it has remained a very challenging topic over the past few decades.
In this thesis, an automatic method is proposed for multiple pedestrian tracking.
State-of-the-art detection and tracking algorithms have been used in this study, followed by a novel post stage processing to increase the accuracy.
Proposed automatic tracking system was compared with a state-of-the-art tracking algorithm which shows comparable accuracy when used with the original incomplete ground truth data. It is estimated to offer better accuracy with a more accurate ground truth data.
The proposed algorithm offers potential improvements in both true positive ratio as well as false negative ratio when compared with the existing algorithm.
Our method is applicable in both outdoor applications such as tracking pedestrians that are walking in the street as well as indoor applications such as tracking people inside a building.
Subjects/Keywords: Multiple Pedestrian Tracking; Deep Learning; Object Tracking; Computer Engineering
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ramin, M. (2016). Improvements to Tracking Pedestrians in Video Streams Using a Pre-trained Convolutional Neural Network. (Thesis). University of Western Ontario. Retrieved from https://ir.lib.uwo.ca/etd/3886
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Ramin, Marjan. “Improvements to Tracking Pedestrians in Video Streams Using a Pre-trained Convolutional Neural Network.” 2016. Thesis, University of Western Ontario. Accessed March 08, 2021.
https://ir.lib.uwo.ca/etd/3886.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Ramin, Marjan. “Improvements to Tracking Pedestrians in Video Streams Using a Pre-trained Convolutional Neural Network.” 2016. Web. 08 Mar 2021.
Vancouver:
Ramin M. Improvements to Tracking Pedestrians in Video Streams Using a Pre-trained Convolutional Neural Network. [Internet] [Thesis]. University of Western Ontario; 2016. [cited 2021 Mar 08].
Available from: https://ir.lib.uwo.ca/etd/3886.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Ramin M. Improvements to Tracking Pedestrians in Video Streams Using a Pre-trained Convolutional Neural Network. [Thesis]. University of Western Ontario; 2016. Available from: https://ir.lib.uwo.ca/etd/3886
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

York University
24.
Chen, Bao Xin.
Real-Time Online Human Tracking with a Stereo Camera for Person-Following Robots.
Degree: MSc -MS, Computer Science, 2020, York University
URL: https://yorkspace.library.yorku.ca/xmlui/handle/10315/37376
► Person-Following Robots have been studied for multiple decades now. Recently, person-following robots have relied on various sensors (e.g., radar, infrared, laser, ultrasonic, etc). However, these…
(more)
▼ Person-Following Robots have been studied for multiple decades now. Recently, person-following robots have relied on various sensors (e.g., radar, infrared, laser, ultrasonic, etc). However, these technologies lack the use of the most reliable information from visible colors (visible light cameras) for high-level perception; therefore, many of them are not stable when the robot is placed under complex environments (e.g., crowded scenes, occlusion, target disappearance, etc.). In this thesis, we are presenting three different approaches to track a human target for person-following robots in challenging situations (e.g., partial and full occlusions, appearance changes, pose changes, illumination changes, or distractor wearing the similar clothes, etc.) with a stereo depth camera. The newest tracker (SiamMDH, a Siamese convolutional neural network based tracker with temporary appearance model) implemented in this work achieves 98.92% accuracy with location error threshold 50 pixels and 92.94% success rate with IoU threshold 0.5 on our extensive person-following dataset.
Advisors/Committee Members: Tsotsos, John K. (advisor).
Subjects/Keywords: Robotics; Person-following robots; Human following; Human tracking; Object tracking
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
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APA (6th Edition):
Chen, B. X. (2020). Real-Time Online Human Tracking with a Stereo Camera for Person-Following Robots. (Masters Thesis). York University. Retrieved from https://yorkspace.library.yorku.ca/xmlui/handle/10315/37376
Chicago Manual of Style (16th Edition):
Chen, Bao Xin. “Real-Time Online Human Tracking with a Stereo Camera for Person-Following Robots.” 2020. Masters Thesis, York University. Accessed March 08, 2021.
https://yorkspace.library.yorku.ca/xmlui/handle/10315/37376.
MLA Handbook (7th Edition):
Chen, Bao Xin. “Real-Time Online Human Tracking with a Stereo Camera for Person-Following Robots.” 2020. Web. 08 Mar 2021.
Vancouver:
Chen BX. Real-Time Online Human Tracking with a Stereo Camera for Person-Following Robots. [Internet] [Masters thesis]. York University; 2020. [cited 2021 Mar 08].
Available from: https://yorkspace.library.yorku.ca/xmlui/handle/10315/37376.
Council of Science Editors:
Chen BX. Real-Time Online Human Tracking with a Stereo Camera for Person-Following Robots. [Masters Thesis]. York University; 2020. Available from: https://yorkspace.library.yorku.ca/xmlui/handle/10315/37376

King Abdullah University of Science and Technology
25.
Mueller, Matthias.
Persistent Aerial Tracking.
Degree: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, 2016, King Abdullah University of Science and Technology
URL: http://hdl.handle.net/10754/608605
► In this thesis, we propose a new aerial video dataset and benchmark for low altitude UAV target tracking, as well as, a photo-realistic UAV simulator…
(more)
▼ In this thesis, we propose a new aerial video dataset and benchmark for low altitude UAV target
tracking, as well as, a photo-realistic UAV simulator that can be coupled with
tracking methods. Our benchmark provides the first evaluation of many state of-the-art and popular trackers on 123 new and fully annotated HD video sequences captured from a low-altitude aerial perspective. Among the compared trackers, we determine which ones are the most suitable for UAV
tracking both in terms of
tracking accuracy and run-time. We also present a simulator that can be used to evaluate
tracking algorithms in real-time scenarios before they are deployed on a UAV ”in the field”, as well as, generate synthetic but photo-realistic
tracking datasets with free ground truth annotations to easily extend existing real-world datasets. Both the benchmark and simulator will be made publicly available to the vision community to further research in the area of
object tracking from UAVs. Additionally, we propose a persistent, robust and autonomous
object tracking system for unmanned aerial vehicles (UAVs) called Persistent Aerial
Tracking (PAT). A computer vision and control strategy is applied to a diverse set of moving objects (e.g. humans, animals, cars, boats, etc.) integrating multiple UAVs with a stabilized RGB camera. A novel strategy is employed to successfully track objects over a long period, by ’handing over the camera’ from one UAV to another. We integrate the complete system into an off- 4 the-shelf UAV, and obtain promising results showing the robustness of our solution in real-world aerial scenarios.
Advisors/Committee Members: Ghanem, Bernard (advisor), Shamma, Jeff S. (committee member), Wonka, Peter (committee member).
Subjects/Keywords: object tracking; Aerial tracking; UAV; surveillance; Benchmark; Simulator
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Mueller, M. (2016). Persistent Aerial Tracking. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/608605
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Mueller, Matthias. “Persistent Aerial Tracking.” 2016. Thesis, King Abdullah University of Science and Technology. Accessed March 08, 2021.
http://hdl.handle.net/10754/608605.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Mueller, Matthias. “Persistent Aerial Tracking.” 2016. Web. 08 Mar 2021.
Vancouver:
Mueller M. Persistent Aerial Tracking. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2016. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/10754/608605.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Mueller M. Persistent Aerial Tracking. [Thesis]. King Abdullah University of Science and Technology; 2016. Available from: http://hdl.handle.net/10754/608605
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
26.
Ambardekar, Amol.
Vehicle Classification Framework: Online Classification with Tracking.
Degree: 2012, University of Nevada – Reno
URL: http://hdl.handle.net/11714/3546
► Video surveillance has significant application prospects such as security, law enforcement, and traffic monitoring. Visual traffic surveillance using computer vision techniques can be non-invasive, cost…
(more)
▼ Video surveillance has significant application prospects such as security, law enforcement, and traffic monitoring. Visual traffic surveillance using computer vision techniques can be non-invasive, cost effective and automated. Detecting and recognizing the objects in a video is an important part of many video surveillance systems which can help in
tracking of the detected objects and gathering important information. In case of traffic video surveillance, vehicle detection and classification is important as it can help in traffic control and gathering of traffic statistics that can be used in intelligent transportation systems. Vehicle classification poses a difficult problem as vehicles have high intra class variation and relatively low inter class variation. In this work, we investigate five different
object recognition techniques: PCA+DFVS, PCA+DIVS, PCA+SVM, LDA, and constellation based modeling applied to the problem of vehicle classification. We also compare them with the state-of-the-art techniques in vehicle classification. In case of the PCA based approaches, we extend face detection using a PCA approach for the problem of vehicle classification to carry out multi-class classification. We also implement constellation model-based approach that uses the dense representation of SIFT features. We consider three classes: sedans, vans, and taxis and record classification accuracy as high as 99.25% in case of cars vs vans and 97.57% in case of sedans vs taxis. We also present a fusion approach that uses both PCA+DFVS and PCA+DIVS and achieves classification accuracy of 96.42% in case of sedans vs vans vs taxis. We incorporated three of the techniques that performed well into a unified traffic surveillance system for online classification of vehicles which uses
tracking results to improve the classification accuracy. We processed 31 minutes of traffic video containing multi-lane traffic intersection to evaluate the accuracy of the system. We were able to achieve classification accuracy as high as 90.49% while classifying correctly tracked vehicles into four classes: Cars, SUVs/Vans, Pickup Trucks, and Buses/Semis. While processing a video, our system also records important traffic parameters such as color of a vehicle, speed of a vehicle, etc. This information was later used in a search assistant tool (SAT) to find interesting traffic events. For the evaluation of video surveillance applications that employ an
object classification module, it is important to establish the ground truth. However, it is a time consuming process when done manually. We developed a ground truth verification tool (GTVT) that can help in this process by automating some of the work.
Advisors/Committee Members: Nicolescu, Mircea (advisor), Bebis, George (committee member), Nicolescu, Monica (committee member), Tian, Zong (committee member), Pinsky, Mark (committee member).
Subjects/Keywords: Computer Vision; Object Classification; Object Recognition; Object Tracking; Traffic Surveillance; Video Surveillance
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ambardekar, A. (2012). Vehicle Classification Framework: Online Classification with Tracking. (Thesis). University of Nevada – Reno. Retrieved from http://hdl.handle.net/11714/3546
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Ambardekar, Amol. “Vehicle Classification Framework: Online Classification with Tracking.” 2012. Thesis, University of Nevada – Reno. Accessed March 08, 2021.
http://hdl.handle.net/11714/3546.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Ambardekar, Amol. “Vehicle Classification Framework: Online Classification with Tracking.” 2012. Web. 08 Mar 2021.
Vancouver:
Ambardekar A. Vehicle Classification Framework: Online Classification with Tracking. [Internet] [Thesis]. University of Nevada – Reno; 2012. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/11714/3546.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Ambardekar A. Vehicle Classification Framework: Online Classification with Tracking. [Thesis]. University of Nevada – Reno; 2012. Available from: http://hdl.handle.net/11714/3546
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Clemson University
27.
Hunde, Andinet Negash.
Multi Sensor Multi Target Perception and Tracking for Informed Decisions in Public Road Scenarios.
Degree: PhD, Automotive Engineering, 2020, Clemson University
URL: https://tigerprints.clemson.edu/all_dissertations/2692
► Multi-target tracking in public traffic calls for a tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. Besides,…
(more)
▼ Multi-target
tracking in public traffic calls for a
tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. Besides, the key problem of data association needs to be handled effectively considering the limitations in the computational resources on-board an autonomous car. The challenge of the
tracking problem is further evident in the use of high-resolution automotive sensors which return multiple detections per
object. Furthermore, it is customary to use multiple sensors that cover different and/or over-lapping Field of View and fuse sensor detections to provide robust and reliable
tracking. As a consequence, in high-resolution multi-sensor settings, the data association uncertainty, and the corresponding
tracking complexity increases pointing to a systematic approach to handle and process sensor detections.
In this work, we present a multi-target
tracking system that addresses target birth/initiation and death/termination processes with automatic track management features. These
tracking functionalities can help facilitate perception during common events in public traffic as participants (suddenly) change lanes, navigate intersections, overtake and/or brake in emergencies, etc. Various
tracking approaches including the ones based on joint integrated probability data association (JIPDA) filter, Linear Multi-target Integrated Probabilistic Data Association (LMIPDA) Filter, and their multi-detection variants are adapted to specifically include algorithms that handle track initiation and termination, clutter density estimation and track management. The utility of the filtering module is further elaborated by integrating it into a trajectory
tracking problem based on model predictive control.
To cope with
tracking complexity in the case of multiple high-resolution sensors, we propose a hybrid scheme that combines the approaches of data clustering at the local sensor and multiple detections
tracking schemes at the fusion layer. We implement a track-to-track fusion scheme that de-correlates local (sensor) tracks to avoid double counting and apply a measurement partitioning scheme to re-purpose the LMIPDA
tracking algorithm to multi-detection cases. In addition to the measurement partitioning approach, a joint extent and kinematic state estimation scheme are integrated into the LMIPDA approach to facilitate perception and
tracking of an individual as well as group targets as applied to multi-lane public traffic. We formulate the
tracking problem as a two hierarchical layer. This arrangement enhances the multi-target
tracking performance in situations including but not limited to target initialization(birth process), target occlusion, missed detections, unresolved measurement, target maneuver, etc. Also, target groups expose complex individual target interactions to help in situation assessment which is challenging to capture otherwise.
The simulation studies are complemented by experimental studies performed on single and…
Advisors/Committee Members: Yunyi Jia, Zoran Filipi, Ioannis Karamouzas, Bing Li.
Subjects/Keywords: Autonomous Perception and Tracking; Camera and Radar Sensor Fusion; Extended Object Tracking; Group Target Tracking; Multi-Target Tracking; Public Traffic
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hunde, A. N. (2020). Multi Sensor Multi Target Perception and Tracking for Informed Decisions in Public Road Scenarios. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/2692
Chicago Manual of Style (16th Edition):
Hunde, Andinet Negash. “Multi Sensor Multi Target Perception and Tracking for Informed Decisions in Public Road Scenarios.” 2020. Doctoral Dissertation, Clemson University. Accessed March 08, 2021.
https://tigerprints.clemson.edu/all_dissertations/2692.
MLA Handbook (7th Edition):
Hunde, Andinet Negash. “Multi Sensor Multi Target Perception and Tracking for Informed Decisions in Public Road Scenarios.” 2020. Web. 08 Mar 2021.
Vancouver:
Hunde AN. Multi Sensor Multi Target Perception and Tracking for Informed Decisions in Public Road Scenarios. [Internet] [Doctoral dissertation]. Clemson University; 2020. [cited 2021 Mar 08].
Available from: https://tigerprints.clemson.edu/all_dissertations/2692.
Council of Science Editors:
Hunde AN. Multi Sensor Multi Target Perception and Tracking for Informed Decisions in Public Road Scenarios. [Doctoral Dissertation]. Clemson University; 2020. Available from: https://tigerprints.clemson.edu/all_dissertations/2692
28.
Shanmuga Vadivel, Karthikeyan.
Modeling Eye Tracking Data with Application to Object Detection.
Degree: 2014, University of California – eScholarship, University of California
URL: http://www.escholarship.org/uc/item/556687hq
► This research focuses on enhancing computer vision algorithms using eye tracking and visual saliency. Recent advances in eye tracking device technology have enabled large scale…
(more)
▼ This research focuses on enhancing computer vision algorithms using eye tracking and visual saliency. Recent advances in eye tracking device technology have enabled large scale collection of eye tracking data, without affecting viewer experience. As eye tracking data is biased towards high level image and video semantics, it provides a valuable prior for object detection in images and object extraction in videos. We specifically explore the following problems in the thesis: 1) eye tracking and saliency enhanced object detection, 2) eye tracking assisted object extraction in videos, and 3) role of object co-occurrence and camera focus in visual attention modeling.Since human attention is biased towards faces and text, in the first work we propose an approach to isolate face and text regions in images by analyzing eye tracking data from multiple subjects. Eye tracking data is clustered and region labels are predicted using a Markov random field model. In the second work, we study object extraction in videos using eye tracking prior. We propose an algorithm to extract dominant visual tracks in eye tracking data from multiple subjects by solving a linear assignment problem. Visual tracks localize object search and we propose a novel mixed graph association framework, inferred by binary integer linear programming. In the final work, we address the problem of predicting where people look in images. We specifically explore the importance of scene context in the form of object co-occurrence and camera focus. The proposed model extracts low-, mid- and high-level and scene context features and uses a regression framework to predict visual attention map. In all the above cases, extensive experimental results show that the proposed methods outperform current state-of-the-art.
Subjects/Keywords: Engineering; Electrical engineering; Computer science; Eye Tracking; Multiple Object Tracking; Object Detection; Text Detection; Visual Saliency
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Shanmuga Vadivel, K. (2014). Modeling Eye Tracking Data with Application to Object Detection. (Thesis). University of California – eScholarship, University of California. Retrieved from http://www.escholarship.org/uc/item/556687hq
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Shanmuga Vadivel, Karthikeyan. “Modeling Eye Tracking Data with Application to Object Detection.” 2014. Thesis, University of California – eScholarship, University of California. Accessed March 08, 2021.
http://www.escholarship.org/uc/item/556687hq.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Shanmuga Vadivel, Karthikeyan. “Modeling Eye Tracking Data with Application to Object Detection.” 2014. Web. 08 Mar 2021.
Vancouver:
Shanmuga Vadivel K. Modeling Eye Tracking Data with Application to Object Detection. [Internet] [Thesis]. University of California – eScholarship, University of California; 2014. [cited 2021 Mar 08].
Available from: http://www.escholarship.org/uc/item/556687hq.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Shanmuga Vadivel K. Modeling Eye Tracking Data with Application to Object Detection. [Thesis]. University of California – eScholarship, University of California; 2014. Available from: http://www.escholarship.org/uc/item/556687hq
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

UCLA
29.
Taylor, Brian.
Leveraging Occlusion Cues for Causal Video Object Segmentation.
Degree: Computer Science, 2016, UCLA
URL: http://www.escholarship.org/uc/item/3xz0z5k9
► This thesis describes a framework leveraging occlusions as a cue for detecting objects and accurately localizing their boundaries throughout the course of a video. Triggered…
(more)
▼ This thesis describes a framework leveraging occlusions as a cue for detecting objects and accurately localizing their boundaries throughout the course of a video. Triggered by the motion of objects in the scene, occlusions provide coarse knowledge of the spatial relationship of objects with respect to the viewer. While effective for detecting objects when motion is sufficient, we explore ways to reliably detect and track objects when motion is inadequate or difficult to estimate.In the first half, we incorporate semantic classifiers to provide cues when occlusions are weak, and observe occlusion and appearance information to be mutually beneficial, yielding results more resilient to failures of the component systems acting alone. Our system is evaluated on the semantic segmentation task. In the latter half, we drop semantics and instead devise a causal framework integrating segmentation results and occlusion cues from frames processed in the past. So long as objects move sufficiently with respect to the viewer at some point, they will be detected and subsequently tracked for the rest of the video. We evaluated our approach on the video object segmentation problem. The resulting system has the capability to automatically discover objects from occlusions in video and track their shapes as they evolve over time. Coarse depth is provided as a byproduct and the assignment of semantic category labels can be integrated in a natural way.
Subjects/Keywords: Computer science; depth layer segmentation; object tracking; occlusions; video object segmentation; video segmentation
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Taylor, B. (2016). Leveraging Occlusion Cues for Causal Video Object Segmentation. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/3xz0z5k9
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Taylor, Brian. “Leveraging Occlusion Cues for Causal Video Object Segmentation.” 2016. Thesis, UCLA. Accessed March 08, 2021.
http://www.escholarship.org/uc/item/3xz0z5k9.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Taylor, Brian. “Leveraging Occlusion Cues for Causal Video Object Segmentation.” 2016. Web. 08 Mar 2021.
Vancouver:
Taylor B. Leveraging Occlusion Cues for Causal Video Object Segmentation. [Internet] [Thesis]. UCLA; 2016. [cited 2021 Mar 08].
Available from: http://www.escholarship.org/uc/item/3xz0z5k9.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Taylor B. Leveraging Occlusion Cues for Causal Video Object Segmentation. [Thesis]. UCLA; 2016. Available from: http://www.escholarship.org/uc/item/3xz0z5k9
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
30.
Budi, Sugandi.
Study on Object Tracking Under Outdoor Environments Using Video Cameras : ビデオカメラを用いた屋外環境下での物体追跡に関する研究.
Degree: 博士(工学), 2017, Kyushu Institute of Technology / 九州工業大学
URL: http://hdl.handle.net/10228/4886
► In this thesis, we investigated the development of the object tracking system including object detection, object tracking and object identification/recognition and related issues. We explored…
(more)
▼ In this thesis, we investigated the development of the
object tracking system including
object detection,
object tracking and
object identification/recognition and related issues. We explored the related
object tracking system methodology and the future development of the
tracking system. We have presented various recent methodology of the
tracking system including
object detection,
object tracking and
object recognition. On each methodology, we presented detail methodology with the weakness and the strong points. Furthermore, we presented also the future development of the
tracking system.In this thesis, our proposed method is applied into two categories of
object tracking system that based on deterministic algorithm and stochastic algorithm. On the first approach, we divide our study on three main categories for building an automated
tracking system, which can be listed as
object detection,
object tracking and
object identification. We introduce our proposed method on each category. We propose low resolution image on frame difference to detect the moving
object. We proposed a block matching based on PISC image to track the interest
object. In addition, to evaluate the method, we proposed an identification method based on the color and spatial features. Our proposed method can achieve satisfactory result with the identification rate of 92.1[%] in average. In order to increase the field of view of camera, we propose also
object tracking using multi-camera. We implement the multi-camera system under LAN environment which each camera connected to each PC. We successfully track the interest
object using two camera and we obtain the wider view of camera. Our proposed method can achieve the detection rates of 97.23[%] and accuracy of 96.98[%] in average.On the second approach, we proposed a color-based particle filter for single
object and multiple objects
tracking. On this approach, we rely on the color likelihood as an image measurement to estimate the state of the moving objects. In addition, to handle the appearance change and background clutter, we proposed also model updating. We analyzed the effect of the number of particles and number of histogram bins to the processing time and
tracking accuracy. We obtained that the processing time is related Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Japan Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Japan to the number of particles and number of histogram although the
tracking accuracy increase also. The experimental results show the algorithm can successfully track the single moving
object based on known and unknown initial position and
object appearance. Finally, we proposed to expand the color-based particle filter algorithm to track multiple objects in the presence of occlusion. We proposed to handle occlusion by redefining the resample and model update step. The occlusion itself is predicted based on the estimated position and likelihood measurement. We implemented our proposed algorithm to track…
Advisors/Committee Members: 金, 亨燮.
Subjects/Keywords: Object tracking; PISC image; Particle filter; Color feature; Object occlusion; Multi-camera
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Budi, S. (2017). Study on Object Tracking Under Outdoor Environments Using Video Cameras : ビデオカメラを用いた屋外環境下での物体追跡に関する研究. (Thesis). Kyushu Institute of Technology / 九州工業大学. Retrieved from http://hdl.handle.net/10228/4886
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Budi, Sugandi. “Study on Object Tracking Under Outdoor Environments Using Video Cameras : ビデオカメラを用いた屋外環境下での物体追跡に関する研究.” 2017. Thesis, Kyushu Institute of Technology / 九州工業大学. Accessed March 08, 2021.
http://hdl.handle.net/10228/4886.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Budi, Sugandi. “Study on Object Tracking Under Outdoor Environments Using Video Cameras : ビデオカメラを用いた屋外環境下での物体追跡に関する研究.” 2017. Web. 08 Mar 2021.
Vancouver:
Budi S. Study on Object Tracking Under Outdoor Environments Using Video Cameras : ビデオカメラを用いた屋外環境下での物体追跡に関する研究. [Internet] [Thesis]. Kyushu Institute of Technology / 九州工業大学; 2017. [cited 2021 Mar 08].
Available from: http://hdl.handle.net/10228/4886.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Budi S. Study on Object Tracking Under Outdoor Environments Using Video Cameras : ビデオカメラを用いた屋外環境下での物体追跡に関する研究. [Thesis]. Kyushu Institute of Technology / 九州工業大学; 2017. Available from: http://hdl.handle.net/10228/4886
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
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