Robust object tracking based on sparse representation

Shengping Zhang; Hongxun Yao; Xin Sun; Shaohui Liu

doi:10.1117/12.863437

4 August 2010 Robust object tracking based on sparse representation

Shengping Zhang, Hongxun Yao, Xin Sun, Shaohui Liu

Proceedings Volume 7744, Visual Communications and Image Processing 2010; 77441N (2010) https://doi.org/10.1117/12.863437
Event: Visual Communications and Image Processing 2010, 2010, Huangshan, China

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.

Citation Download Citation

Shengping Zhang, Hongxun Yao, Xin Sun, and Shaohui Liu "Robust object tracking based on sparse representation", Proc. SPIE 7744, Visual Communications and Image Processing 2010, 77441N (4 August 2010); https://doi.org/10.1117/12.863437

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