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Proceedings Paper

Robust object tracking based on sparse representation
Author(s): Shengping Zhang; Hongxun Yao; Xin Sun; Shaohui Liu
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Paper Abstract

In this paper, we propose a novel and robust object tracking algorithm based on sparse representation. Object tracking is formulated as a object recognition problem rather than a traditional search problem. All target candidates are considered as training samples and the target template is represented as a linear combination of all training samples. The combination coefficients are obtained by solving for the minimum l1-norm solution. The final tracking result is the target candidate associated with the non-zero coefficient. Experimental results on two challenging test sequences show that the proposed method is more effective than the widely used mean shift tracker.

Paper Details

Date Published: 4 August 2010
PDF: 8 pages
Proc. SPIE 7744, Visual Communications and Image Processing 2010, 77441N (4 August 2010); doi: 10.1117/12.863437
Show Author Affiliations
Shengping Zhang, Harbin Institute of Technology (China)
Hongxun Yao, Harbin Institute of Technology (China)
Xin Sun, Harbin Institute of Technology (China)
Shaohui Liu, Harbin Institute of Technology (China)

Published in SPIE Proceedings Vol. 7744:
Visual Communications and Image Processing 2010
Pascal Frossard; Houqiang Li; Feng Wu; Bernd Girod; Shipeng Li; Guo Wei, Editor(s)

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