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Delft University of Technology

1. Katsaounis, Georgios (author). Extended Object Tracking of Pedestrians in Automotive Applications.

Degree: 2019, Delft University of Technology

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 (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 18, 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. 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

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

Degree: Automatic Control, 2019, Linköping University

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

…environment. The tracking system uses a camera sensor for monitoring objects, such as vehicles and… …information about the environment [8]. The product inputs images from the camera sensor… …systems. The camera projects the world coordinates onto the image sensor, which is modeled as a… …input to the model. 3.4.1 Region of Interest From the camera image sensor the objects are… …of the pinhole camera model . . . . . . . . . . . . . Perfect ROI markings done by hand… 

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

APA (6th 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 (16th 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 (7th 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

.