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University of Waterloo

1. Balasubramanian, Venkateshwaran. 3D Online Multi-Object Tracking for Autonomous Driving.

Degree: 2019, University of Waterloo

This research work focuses on exploring a novel 3D multi-object tracking architecture: 'FANTrack: 3D Multi-Object Tracking with Feature Association Network' for autonomous driving, based on tracking by detection and online tracking strategies using deep learning architectures for data association. The problem of multi-target tracking aims to assign noisy detections to a-priori unknown and time-varying number of tracked objects across a sequence of frames. A majority of the existing solutions focus on either tediously designing cost functions or formulating the task of data association as a complex optimization problem that can be solved effectively. Instead, we exploit the power of deep learning to formulate the data association problem as inference in a CNN. To this end, we propose to learn a similarity function that combines cues from both image and spatial features of objects. The proposed approach consists of a similarity network that predicts the similarity scores of the object pairs and builds a local similarity map. Another network formulates the data association problem as inference in a CNN by using the similarity scores and spatial information. The model learns to perform global assignments in 3D purely from data, handles noisy detections and a varying number of targets, and is easy to train. Experiments on the challenging Kitti dataset show competitive results with the state of the art. The model is finally implemented in ROS and deployed on our autonomous vehicle to show the robustness and online tracking capabilities. The proposed tracker runs alongside the object detector utilizing the resources efficiently.

Subjects/Keywords: multi-object tracking; perception; computer vision; deep learning; autonomous driving

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

APA (6th Edition):

Balasubramanian, V. (2019). 3D Online Multi-Object Tracking for Autonomous Driving. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14994

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):

Balasubramanian, Venkateshwaran. “3D Online Multi-Object Tracking for Autonomous Driving.” 2019. Thesis, University of Waterloo. Accessed September 19, 2019. http://hdl.handle.net/10012/14994.

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

MLA Handbook (7th Edition):

Balasubramanian, Venkateshwaran. “3D Online Multi-Object Tracking for Autonomous Driving.” 2019. Web. 19 Sep 2019.

Vancouver:

Balasubramanian V. 3D Online Multi-Object Tracking for Autonomous Driving. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/10012/14994.

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

Council of Science Editors:

Balasubramanian V. 3D Online Multi-Object Tracking for Autonomous Driving. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14994

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

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