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dc.contributor.authorBalasubramanian, Venkateshwaran 14:58:24 (GMT) 14:58:24 (GMT)
dc.description.abstractThis 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.en
dc.publisherUniversity of Waterlooen
dc.subjectmulti-object trackingen
dc.subjectcomputer visionen
dc.subjectdeep learningen
dc.subjectautonomous drivingen
dc.title3D Online Multi-Object Tracking for Autonomous Drivingen
dc.typeMaster Thesisen
dc.pendingfalse R. Cheriton School of Computer Scienceen Scienceen of Waterlooen
uws-etd.degreeMaster of Mathematicsen
uws.contributor.advisorCzarnecki, Krzysztof
uws.contributor.affiliation1Faculty of Mathematicsen

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