subject:(Extended Object Tracking)

Clemson University

1. 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 (6^th 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 (16^th Edition):

Hunde, Andinet Negash. “Multi Sensor Multi Target Perception and Tracking for Informed Decisions in Public Road Scenarios.” 2020. Doctoral Dissertation, Clemson University. Accessed January 18, 2021. https://tigerprints.clemson.edu/all_dissertations/2692.

MLA Handbook (7^th Edition):

Hunde, Andinet Negash. “Multi Sensor Multi Target Perception and Tracking for Informed Decisions in Public Road Scenarios.” 2020. Web. 18 Jan 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 Jan 18]. 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

Delft University of Technology

2. 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Katsaounis, Georgios (author). “Extended Object Tracking of Pedestrians in Automotive Applications.” 2019. Masters Thesis, Delft University of Technology. Accessed January 18, 2021. http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749.

MLA Handbook (7^th Edition):

Katsaounis, Georgios (author). “Extended Object Tracking of Pedestrians in Automotive Applications.” 2019. Web. 18 Jan 2021.

Vancouver:

Katsaounis G(. Extended Object Tracking of Pedestrians in Automotive Applications. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 18]. 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

3. Khajo, Gabriel. Region Proposal Based Object Detectors Integrated With an Extended Kalman Filter for a Robust Detect-Tracking Algorithm.

Degree: Science and Technology (starting 2013), 2019, Karlstad University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72698

► In this thesis we present a detect-tracking algorithm (see figure 3.1) that combines the detection robustness of static region proposal based object detectors, like… (more)

▼ In this thesis we present a detect-tracking algorithm (see figure 3.1) that combines the detection robustness of static region proposal based object detectors, like the faster region convolutional neural network (R-CNN) and the region-based fully convolutional networks (R-FCN) model, with the tracking prediction strength of extended Kalman filters, by using, what we have called, a translating and non-rigid user input region of interest (RoI-) mapping. This so-called RoI-mapping maps a region, which includes the object that one is interested in tracking, to a featureless three-channeled image. The detection part of our proposed algorithm is then performed on the image that includes only the RoI features (see figure 3.2). After the detection step, our model re-maps the RoI features to the original frame, and translates the RoI to the center of the prediction. If no prediction occurs, our proposed model integrates a temporal dependence through a Kalman filter as a predictor; this filter is continuously corrected when detections do occur. To train the region proposal based object detectors that we integrate into our detect-tracking model, we used TensorFlow^®’s object detection api, with a random search hyperparameter tuning, where we fine-tuned, all models from TensorFlow^® slim base network classification checkpoints. The trained region proposal based object detectors used the inception V2 base network for the faster R-CNN model and the R-FCN model, while the inception V3 base network only was applied to the faster R-CNN model. This was made to compare the two base networks and their corresponding affects on the detection models. In addition to the deep learning part of this thesis, for the implementation part of our detect-tracking model, like for the extended Kalman filter, we used Python and OpenCV^® . The results show that, with a stationary camera reference frame, our proposed detect-tracking algorithm, combined with region proposal based object detectors on images of size 414 × 740 × 3, can detect and track a small object in real-time, like a tennis ball, moving along a horizontal trajectory with an average velocity v ≈ 50 km/h at a distance d = 25 m, with a combined detect-tracking frequency of about 13 to 14 Hz. The largest measured state error between the actual state and the predicted state from the Kalman filter, at the aforementioned horizontal velocity, have been measured to be a maximum of 10-15 pixels, see table 5.1, but in certain frames where many detections occur this error has been shown to be much smaller (3-5 pixels). Additionally, our combined detect-tracking model has also been shown to be able to handle obstacles and two learnable features that overlap, thanks to the integrated extended Kalman filter. Lastly, our detect-tracking model also was applied on a set of infra-red images, where the goal was to detect and track a moving truck moving along a semi-horizontal path. Our results show that a faster R-CNN inception V2 model was able to extract features…

Subjects/Keywords: Object Detection; Extended Kalman Filter; Tracking; Computer Vision and Robotics (Autonomous Systems); Datorseende och robotik (autonoma system)

…object detector side by side with a classical computer vision tracking algorithm, like a… …object using earlier measurements/detections, the filter might be able to continue tracking the… …Detect-Tracking Model Applied to Rolling Ball Moving at 20 km/h at d = 20 m. . . . Detect… …Tracking Model Applied to Rolling Ball Moving at � 47 km/h at d = 25 m . . . Detect-Tracking… …Model Applied to Rolling Ball Moving at � 53 km/h at d = 25 m . . . Detect-Tracking Model…

13 more images…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Khajo, G. (2019). Region Proposal Based Object Detectors Integrated With an Extended Kalman Filter for a Robust Detect-Tracking Algorithm. (Thesis). Karlstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72698

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Khajo, Gabriel. “Region Proposal Based Object Detectors Integrated With an Extended Kalman Filter for a Robust Detect-Tracking Algorithm.” 2019. Thesis, Karlstad University. Accessed January 18, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72698.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Khajo, Gabriel. “Region Proposal Based Object Detectors Integrated With an Extended Kalman Filter for a Robust Detect-Tracking Algorithm.” 2019. Web. 18 Jan 2021.

Vancouver:

Khajo G. Region Proposal Based Object Detectors Integrated With an Extended Kalman Filter for a Robust Detect-Tracking Algorithm. [Internet] [Thesis]. Karlstad University; 2019. [cited 2021 Jan 18]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72698.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Khajo G. Region Proposal Based Object Detectors Integrated With an Extended Kalman Filter for a Robust Detect-Tracking Algorithm. [Thesis]. Karlstad University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72698

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

4. Biswas, Srijanee. Goal-Aware Robocentric Mapping and Navigation of a Quadrotor Unmanned Aerial Vehicle.

Degree: MS, Engineering and Applied Science: Aerospace Engineering, 2019, University of Cincinnati

URL: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552581467878839

► Autonomous vehicles have become a reality in many military and civilian applications. The ability to deploy them in constrained environments, such as regions with limited… (more)

Subjects/Keywords: Engineering; GPS-denied Navigation; Extended Kalman Filter; Obstacle Avoidance; Nonlinear Observability Analysis; Object Tracking; Unmanned Aerial Vehicle

…motion of the object. Visual tracking is accomplished by first detecting an object and then… …form of object tracking is via the use of density-based appearance models such as mean-shift… …several other object detection and tracking strategies are presented in [71, 89]. 1.4… …Localization 1.3.2 Path Planning . . . . . . 1.3.3 Visual Tracking . . . . . . 1.4 Summary of… …2.3.3 Velocity Controller . . . . . . . . . . . 2.4 Visual Tracking…

10 more images…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Biswas, S. (2019). Goal-Aware Robocentric Mapping and Navigation of a Quadrotor Unmanned Aerial Vehicle. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552581467878839

Chicago Manual of Style (16^th Edition):

Biswas, Srijanee. “Goal-Aware Robocentric Mapping and Navigation of a Quadrotor Unmanned Aerial Vehicle.” 2019. Masters Thesis, University of Cincinnati. Accessed January 18, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552581467878839.

MLA Handbook (7^th Edition):

Biswas, Srijanee. “Goal-Aware Robocentric Mapping and Navigation of a Quadrotor Unmanned Aerial Vehicle.” 2019. Web. 18 Jan 2021.

Vancouver:

Biswas S. Goal-Aware Robocentric Mapping and Navigation of a Quadrotor Unmanned Aerial Vehicle. [Internet] [Masters thesis]. University of Cincinnati; 2019. [cited 2021 Jan 18]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552581467878839.

Council of Science Editors:

Biswas S. Goal-Aware Robocentric Mapping and Navigation of a Quadrotor Unmanned Aerial Vehicle. [Masters Thesis]. University of Cincinnati; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552581467878839

5. Vestin, Albin. Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms.

Degree: Automatic Control, 2019, Linköping University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020

► Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking… (more)

▼ Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.

Subjects/Keywords: evaluation; target tracking; multiple sensors; non-causal; smoother; smoothing; tracking; vehicle tracking; camera; lidar; estimate; estimation; prediction; vehicle dynamics; sensor fusion; real-time tracking; extended kalman filter; filter validation; validation; position estimation; velocity estimation; dynamic model; model complexity; multi object tracking; multiple object; tracking; single object tracking; data association; tracking fundamentals; iterated kalman filter; track management; gnn; global nearest neighbour; mahalanobis; mahalanobis distance; performance evaluation; differential gps; dgps; roi; ego; several sensors; sensors; rmse; root mean square error; invertible motion; anti-causal motion; anti-causal tracking; constant velocity; gnn; imu; tfs; two filter smoother; ekf; rts; radar; inertial measurement unit; nonlinear; nonlinear systems; mono camera; monocular camera; noise model; tracking performance; fixed interval smoothing; m/n logic; centralized fusion; non-causal object tracker; car tracking; car dynamics; automotive; active safety; object tracking; automotive industry; thesis; master; reverse dynamics; reverse tracking; reverse sequence; sequence tracking; data propagation; ground truth; estimating ground truth; additional sensors; mounted sensors; true estimates; environment; comparison; algorithm; independent targets; overlapping; measurements; occluded; track switch; improve; lower; uncertainty; more; certain; state; process; noise; covariance; sampling; image; sprt; adas; cnn; cv; pdf; track; target; ego; tracker; tentative track; observatiom; online tracking; offline tracking; online; offline; recorded; sequences; robust; self driving; self-driving; car; traffic; trajectory; true state; scenario; scenarios; future; accurate; output; advanced; driver; assistance; systems; non-linear; complex noise; pedestrian; truck; bus; maneuvering; vehicles; processed; measurement; frame; state; correction; probability; density; function; tuning; likelihood; transition; measurement; motion; model; recursion; gaussian; approximation; distribution; linear; jacobian; multiplicative; noise; ratio; ad; hoc; ad hoc; state; space; approach; backward; auction; euclidean; distance; statistical; threshold; gating; association; margin; normalize; covariance; matrix; fusion; confirmed; rejected; tentative; history; absolute; error; modular; ego motion; parameters; variables; logg; hardware; specification; fused; causal; factorization; independent; uncorrelated; transform; moving; rotation; translation; oncoming; overtaking; Control Engineering; Reglerteknik

…24 24 26 27 27 28 30 30 32 33 Performance Evaluation 4.1 Single Object Tracking… …Comparison . . . . . . . 4.2 Multi Object Tracking . . . . . . . . . 4.2.1 Scenario 3: Independent… …5.1.4 Multi Object Tracking . 5.2 Future Work… …Track Observation Online tracking Offline tracking Explanation Object of interest to estimate… …2, we introduce the theoretical background of object tracking including the fundamental…

6 more images…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Vestin, A. (2019). Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Vestin, Albin. “Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms.” 2019. Thesis, Linköping University. Accessed January 18, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Vestin, Albin. “Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms.” 2019. Web. 18 Jan 2021.

Vancouver:

Vestin A. Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms. [Internet] [Thesis]. Linköping University; 2019. [cited 2021 Jan 18]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vestin A. Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms. [Thesis]. Linköping University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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