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Towards efficient search methods in object tracking: An evaluation and application to precise tracking Open Access


Other title
Computer Vision
Tracking Database
Search Method
Type of item
Degree grantor
University of Alberta
Author or creator
Roy, Ankush
Supervisor and department
Jagersand, Martin (Department of Computing Science)
Examining committee member and department
Cobzas, Dana (Department of Computing Science)
Ray, Nilanjan (Department of Computing Science)
Jagersand, Martin (Department of Computing Science)
Department of Computing Science

Date accepted
Graduation date
Master of Science
Degree level
Object tracking is a much researched subject in the computer vision community. With more and more tracking algorithms reported every year, standard benchmarking and evaluation methods are reported for long term tracking systems. We present a public dataset to evaluate trackers used for human and robot manipulation tasks. For these tasks high degrees of freedom (DOF) motion of the object is to be tracked with high accuracy. Both the process of recording the sequences and how ground truth data was generated for the videos is described in detail. As an initial example, the performance of seven published trackers are evaluated. We describe a new evaluation metric to test sensitivity of trackers to speed. A total of 100 annotated and tagged sequences are reported. All the videos, ground truth data, tagged image frames, original implementation of trackers and evaluation scripts are made publicly available. We also introduce a new search method in tracking. Sequential Graph based Approximate Nearest Neighbour Search algorithm or SGANNS. It uses overlapping image features in videos to build a connected graph, offline. This graph is then searched efficiently during tracking to predict the best warp parameters. We test this algorithm on the dataset reported and further analyze the results. Finally we show that using a detection module, registration based trackers can be made more robust. We address tracking challenges of occlusion and varying appearance which a regular registration based tracker fails to track.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
@inproceedings{trackerManipulation, title={Tracking Benchmark and Evaluation for Manipulation Tasks}, author={Roy, Ankush and Zhang, Xi and Wolleb, Nina and Perez, Quenterio, Camilo and Jagersand, Martin}, booktitle={International Conference on Robotics and Automation}, publisher={IEEE}, year={2015} }

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