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Delft University of Technology
1.
Immerzeel, Ronald (author).
Robust Tracking Approach for Dealing with Classification Uncertainty.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:abe30b3f-b2f4-4a4e-9b34-fc42159a0e0e 
► Every year about 1.25 million people die as a result of road traffic accidents. Besides the traffic on the road increases every day, including the…
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▼ Every year about 1.25 million people die as a result of road traffic accidents. Besides the traffic on the road increases every day, including the environmental impact due to the corresponding traffic emissions. Autonomous driving could be a unique opportunity to increase these traffic safety, traffic flow efficiency and to reduce emissions in future. In order to operate reliably and accurately, autonomous driving vehicles and autonomous features require an accurate perception of the infrastructure and other road users in the surrounding. Multi-object tracking is the process concerned with the estimation of the states of the objects in the environment, given the noisy measurements from the sensors. Besides the estimation of the states, it is also necessary to estimate the classification of the objects e.g. for a correct situation analysis and path planning. Classifiers are used to detect and classify objects from image frames, however the classification is sometimes incorrect or uncertain. This results in a decrease of tracking accuracy and an incorrect classification in these classification uncertain conditions. The contribution in this work is, by keeping the classification uncertainty in the tracking approach and using it in all steps, to jointly improve the tracking accuracy as well as the classification.
Mechanical Engineering | BioMechanical Design
Advisors/Committee Members: Happee, Riender (mentor), Domhof, Joris (mentor), Krasnov, Oleg (graduation committee), Kooij, Julian (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Object tracking; Classification; RCA-CPHD; classification uncertainty; Multiple Model CPHD
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APA (6th Edition):
Immerzeel, R. (. (2018). Robust Tracking Approach for Dealing with Classification Uncertainty. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:abe30b3f-b2f4-4a4e-9b34-fc42159a0e0e
Chicago Manual of Style (16th Edition):
Immerzeel, Ronald (author). “Robust Tracking Approach for Dealing with Classification Uncertainty.” 2018. Masters Thesis, Delft University of Technology. Accessed January 28, 2021.
http://resolver.tudelft.nl/uuid:abe30b3f-b2f4-4a4e-9b34-fc42159a0e0e.
MLA Handbook (7th Edition):
Immerzeel, Ronald (author). “Robust Tracking Approach for Dealing with Classification Uncertainty.” 2018. Web. 28 Jan 2021.
Vancouver:
Immerzeel R(. Robust Tracking Approach for Dealing with Classification Uncertainty. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 28].
Available from: http://resolver.tudelft.nl/uuid:abe30b3f-b2f4-4a4e-9b34-fc42159a0e0e.
Council of Science Editors:
Immerzeel R(. Robust Tracking Approach for Dealing with Classification Uncertainty. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:abe30b3f-b2f4-4a4e-9b34-fc42159a0e0e
Delft University of Technology
2.
Wymenga, Jan (author).
Weather Condition Estimation in Automated Vehicles.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94
► This work presents a multi-sensor approach for weather condition estimation in automated vehicles. Using combined data from weather sensors (barometer, hygrometer, etc) and an in-vehicle…
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▼ This work presents a multi-sensor approach for weather condition estimation in automated vehicles. Using combined data from weather sensors (barometer, hygrometer, etc) and an in-vehicle camera, a machine learning and computer vision framework is employed to estimate the current weather condition in realtime and in-vehicle. The use of different sensor types is shown to improve robustness and reduce noise. The resulting modular framework allows it to be used with different sensor configurations, and allows changes in sensor configuration with minimal effort. Finally, a proof-of-concept experiment is presented; a dataset is recorded using a test vehicle and used for model evaluation. The resulting datasets contains 20.000 pairs of video frames and sensor measurements recorded in different weather situations.
ME-BMD-BR
Advisors/Committee Members: Domhof, Joris (mentor), Gavrila, Dariu (graduation committee), Kooij, Julian (graduation committee), Kober, Jens (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: weather types; machine learning; intelligent vehicles; vision; driving; weather
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APA (6th Edition):
Wymenga, J. (. (2018). Weather Condition Estimation in Automated Vehicles. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94
Chicago Manual of Style (16th Edition):
Wymenga, Jan (author). “Weather Condition Estimation in Automated Vehicles.” 2018. Masters Thesis, Delft University of Technology. Accessed January 28, 2021.
http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94.
MLA Handbook (7th Edition):
Wymenga, Jan (author). “Weather Condition Estimation in Automated Vehicles.” 2018. Web. 28 Jan 2021.
Vancouver:
Wymenga J(. Weather Condition Estimation in Automated Vehicles. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 28].
Available from: http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94.
Council of Science Editors:
Wymenga J(. Weather Condition Estimation in Automated Vehicles. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94
Delft University of Technology
3.
Katsaounis, Georgios (author).
Extended Object Tracking of Pedestrians in Automotive Applications.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749
► Recent advances in sensor technology have lead to increased resolution of novel sensors, while tracking applications where distance between sensors and objects of interest is…
(more)
▼ Recent advances in sensor
technology have lead to increased resolution of novel sensors, while tracking applications where distance between sensors and objects of interest is very small have gained research interest recently. In these cases, it is possible that multiple sensor detections are generated by each object of interest. Extended Object Tracking (EOT) approaches consist of algorithms which make use of multiple sensor detections per object to jointly estimate their kinematic and shape extent attributes within the Bayesian tracking framework. In the last decade, various EOT algorithms have been proposed for different types of tracking applications. This M.Sc. thesis project addresses the problem of extended tracking of a single pedestrian walking in the area of a stationary vehicle (referred as ego-vehicle in this report) during a real automotive scenario. The objective is to achieve accurate estimation of both the kinematic attributes (2D centroid position/velocity), as well as its shape extent in x-y plane. In more detail, PreScan software is enabled to design a simulation scenario that is very close to a real automotive application, in terms of motion characteristics of objects of interest and sensor data acquisition. In the considered scenario, different sensor modalities are mounted on the ego-vehicle, namely a Lidar sensor and a mono camera sensor. Moreover, OpenPose library is employed to to obtain pose detections of human body parts from obtained camera images. Concerning shape extent representation, the simplest and most popular approach in previous studies, in general and especially for VRUs tracking, is to assume an elliptical shape. In fact, the Random Matrix Model (RMM), proposed originally by Koch, 2008, is a state-of-the-art EOT state modeling approach that allows for joint estimation of centroid kinematics and physical extent for considered elliptical objects of interest. Based on that, a RMM-based filter using Lidar position measurements has been proposed by Feldmann, 2011. In this project, this algorithm is used as a baseline filter for comparison with our proposed algorithm. In addition, an alternative tracking algorithm is proposed in this study, which has the following differences with respect to the baseline filter: State Initialization of the filter: In our proposed version of the tracking algorithm, human pose detections of shoulders and ankles are are associated with obtained Lidar position measurements in order to provide initial values for the kinematic state (2D position/velocity) and shape parameters (ellipse orientation and semi-axes lengths) of the pedestrian.Measurement Update step of the filter: In our proposed version of the tracking algorithm, camera-obtained pose detections of pedestrian shoulders are associated with obtained Lidar position measurements in order to create an extra measurement, for pedestrian heading angle. Subsequently, a nonlinear filtering update step fusing Lidar-obtained point cloud data for pedestrian position and human-pose-obtained…
Advisors/Committee Members: Alonso Mora, Javier (mentor), Domhof, Joris (mentor), Tasoglou, Athanasios (mentor), Gavrila, Dariu (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Extended Object Tracking; Vulnurable Road Users; Pedestrians; Environmental Perception; Automotive Applications; Lidar sensor; Mono camera sensor; Sensor Fusion; Random Matrix Model; Elliptical shape; OpenPose library; Human Pose Detections; position measurement; heading angle measurement; Extended Kalman Filter; Kalman Filter
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APA (6th Edition):
Katsaounis, G. (. (2019). Extended Object Tracking of Pedestrians in Automotive Applications. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749
Chicago Manual of Style (16th Edition):
Katsaounis, Georgios (author). “Extended Object Tracking of Pedestrians in Automotive Applications.” 2019. Masters Thesis, Delft University of Technology. Accessed January 28, 2021.
http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749.
MLA Handbook (7th Edition):
Katsaounis, Georgios (author). “Extended Object Tracking of Pedestrians in Automotive Applications.” 2019. Web. 28 Jan 2021.
Vancouver:
Katsaounis G(. Extended Object Tracking of Pedestrians in Automotive Applications. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 28].
Available from: http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749.
Council of Science Editors:
Katsaounis G(. Extended Object Tracking of Pedestrians in Automotive Applications. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749
Delft University of Technology
4.
Sahla, Nordin (author).
A Deep Learning Prediction Model for Object Classification.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:f7667cb4-70d4-4b82-ac1b-75df476655cd
► The last decade has marked a rapid and significant growth of the global market of warehouse automation. The biggest challenge lies in the identification and…
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▼ The last decade has marked a rapid and significant growth of the global market of warehouse automation. The biggest challenge lies in the identification and handling of foreign objects. The aim of this research is to investigate whether a usable relation exist between object features such as size or shape, and barcode location, that can be used to robustly identify objects in a bin. A deep convolutional neural network (CNN) is built in MATLAB and trained on a labeled dataset of thousand product images from various perspectives, to determine on which surface of a product the barcode lies. Training results show that while the training set accuracy reaches 100%, a maximum validation accuracy of only 45% is achieved. A larger dataset is required to reduce overfitting and increase the validation accuracy. When sufficient classification accuracies are reached, smart picking strategies can be implemented to efficiently handle products.
Advisors/Committee Members: Wisse, Martijn (mentor), Veeke, Hans (graduation committee), Domhof, Joris (graduation committee), Bharatheesha, Mukunda (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Machine Learning; Convolutional Neural Network; object recognition; Barcode localization
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APA (6th Edition):
Sahla, N. (. (2018). A Deep Learning Prediction Model for Object Classification. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f7667cb4-70d4-4b82-ac1b-75df476655cd
Chicago Manual of Style (16th Edition):
Sahla, Nordin (author). “A Deep Learning Prediction Model for Object Classification.” 2018. Masters Thesis, Delft University of Technology. Accessed January 28, 2021.
http://resolver.tudelft.nl/uuid:f7667cb4-70d4-4b82-ac1b-75df476655cd.
MLA Handbook (7th Edition):
Sahla, Nordin (author). “A Deep Learning Prediction Model for Object Classification.” 2018. Web. 28 Jan 2021.
Vancouver:
Sahla N(. A Deep Learning Prediction Model for Object Classification. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 28].
Available from: http://resolver.tudelft.nl/uuid:f7667cb4-70d4-4b82-ac1b-75df476655cd.
Council of Science Editors:
Sahla N(. A Deep Learning Prediction Model for Object Classification. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:f7667cb4-70d4-4b82-ac1b-75df476655cd
Delft University of Technology
5.
GAO, Xinyu (author).
Sensor Data Fusion of Lidar and Camera for Road User Detection.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a
► Object detection is one of the most important research topics in autonomous vehicles. The detection systems of autonomous vehicles nowadays are mostly image-based ones which…
(more)
▼ Object detection is one of the most important research topics in autonomous vehicles. The detection systems of autonomous vehicles nowadays are mostly image-based ones which detect target objects in the images. Although image-based detectors can provide a rather accurate 2D position of the object in the image, it is necessary to get the accurate 3D position of the object for an autonomous vehicle since it operates in the real 3D world. The relative position of the objects will heavily influence the vehicle control strategy. This thesis work aims to find out a solution for the 3D object detection by combining the Lidar point cloud and camera images, considering that these are two of the most commonly used perception sensors of autonomous vehicles. Lidar performs much better than the camera in 3D object detection since it rebuilds the surface of the surroundings by the point cloud. What’s more, combing Lidar with the camera provides the system redundancy in case of a single sensor failure. Due to the development of Neural Network (NN), past researches achieved great success in detecting objects in the images. Similarly, by applying the deep learning algorithms to parsing the point cloud, the proposed 3D object detection system obtains a competitive result in the KITTI 3D object detection benchmark.
Vehicle Engineering
Advisors/Committee Members: Gavrila, Dariu (mentor), Domhof, Joris (mentor), Kooij, Julian (graduation committee), Pan, Wei (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: 3D object detection; Lidar; Camera; sensor fusion
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APA ·
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MLA ·
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Manager
APA (6th Edition):
GAO, X. (. (2018). Sensor Data Fusion of Lidar and Camera for Road User Detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a
Chicago Manual of Style (16th Edition):
GAO, Xinyu (author). “Sensor Data Fusion of Lidar and Camera for Road User Detection.” 2018. Masters Thesis, Delft University of Technology. Accessed January 28, 2021.
http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a.
MLA Handbook (7th Edition):
GAO, Xinyu (author). “Sensor Data Fusion of Lidar and Camera for Road User Detection.” 2018. Web. 28 Jan 2021.
Vancouver:
GAO X(. Sensor Data Fusion of Lidar and Camera for Road User Detection. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 28].
Available from: http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a.
Council of Science Editors:
GAO X(. Sensor Data Fusion of Lidar and Camera for Road User Detection. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a
. 
Open Access Theses and Dissertations