Visual object tracking via sparse reconstruction
Authors: 
Li Li, Zhenjun Han, Jianbin Jiao, Qixiang YeAuthors Info & Claims
Li Li, Zhenjun Han, Jianbin Jiao, Qixiang YeAuthors Info & ClaimsPages 151 - 154
Abstract
This paper proposes a new approach based on sparse representation for visual object tracking. The sparse representation is implemented by exploiting L1--norm minimization, which most compactly expresses the object and rejects all other possible but less compact representations. With the coefficient vector of the sparse representation, we reconstruct the tracked object in an instantaneous sample set, which improves the tracking adaptation to background variation, object shape change, and partial occlusion. Our experiments on public datasets show state-of-the-art results, which are better than those of several representative tracking methods.
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Index Terms
- Visual object tracking via sparse reconstruction
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Information & Contributors
Information
Published In
December 2010
218 pages
ISBN:9781450304603
DOI:10.1145/1937728
- General Chairs:
- Yong Rui,
- Klara Nahrstedt,
- Xiaofei Xu,
- Program Chairs:
- Hongxun Yao,
- Shuqiang Jiang,
- Jian Cheng
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Association for Computing Machinery
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Published: 30 December 2010
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ICIMCS '10
ICIMCS '10: The Second International Conference on Internet Multimedia Computing and Service
December 30 - 31, 2010
Harbin, China
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Overall Acceptance Rate 163 of 456 submissions, 36%
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