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Proceedings Paper

Coupled data association and l1 minimization for multiple object tracking under occlusion
Author(s): Xue Wang; Qing Wang
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Paper Abstract

We propose a novel multiple object tracking algorithm in a particle filter framework, where the input is a set of candidate regions obtained from Robust Principle Component Analysis (RPCA) in each frame, and the goals is to recover trajectories of objects over time. Our method adapts to the changing appearance of objects, due to occlusion, illumination changes and large pose variations, by incorporating a l1 minimization-based appearance model into the Maximize A Posterior (MAP) inference. Though L1 trackers have showed impressive tracking accuracy, they are computationally demanding for multiple object tracking. Conventional data association methods using simple nonparametric appearance model, such as histogram-based descriptor, may suffer from drastic changing object appearance. The robust tracking performance of our approach has been validated with a comprehensive evaluation involving several challenging sequences and state-of-the-art multiple object trackers.

Paper Details

Date Published: 5 November 2014
PDF: 7 pages
Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 927322 (5 November 2014); doi: 10.1117/12.2073887
Show Author Affiliations
Xue Wang, Northwestern Polytechnical Univ. (China)
Qing Wang, Northwestern Polytechnical Univ. (China)


Published in SPIE Proceedings Vol. 9273:
Optoelectronic Imaging and Multimedia Technology III
Qionghai Dai; Tsutomu Shimura, Editor(s)

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