Techniques for Enhancing the Computational Speed of Multiple Object Tracking
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A massive amount of video data is recorded daily for forensic post analysis and computer vision applications that are used for the analysis of this video data, often perform Multiple Object Tracking (MOT). Advancements in image analysis algorithms and global optimization techniques have improved the accuracy of MOT, often at the cost of slow processing speed which limits its applications to small video datasets. With a focus on fast MOT, this thesis introduces a greedy data association technique (GDA) for MOT, which finds a locally optimum solution with a low computational overhead. To further enhance the computational speed of MOT for large video datasets, three MapReduce-based parallel techniques are introduced. The performance analysis using a set of benchmark video datasets with system prototypes shows that the proposed techniques are significantly faster than the existing state-of-the-art methods.
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Copyright © 2017 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2017
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singh-techniquesforenhancingthecomputationalspeed.pdf | 2023-05-05 | Public | Download |