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Visual tracking with multifeature joint sparse representation

[+] Author Affiliations
Wenhui Dong

Shandong University, School of Control Science and Engineering, 17923 Jingshi Road, Jinan 250061, China

Dezhou University, College of Physics and Electronic Engineering, 566 Daxue Road, Dezhou 253023, China

Faliang Chang

Shandong University, School of Control Science and Engineering, 17923 Jingshi Road, Jinan 250061, China

Zijian Zhao

Shandong University, School of Control Science and Engineering, 17923 Jingshi Road, Jinan 250061, China

J. Electron. Imaging. 24(1), 013006 (Jan 07, 2015). doi:10.1117/1.JEI.24.1.013006
History: Received May 21, 2014; Accepted December 2, 2014
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Abstract.  We present a visual tracking method with feature fusion via joint sparse presentation. The proposed method describes each target candidate by combining different features and joint sparse representation for robustness in coefficient estimation. Then, we build a probabilistic observation model based on the approximation error between the recovered candidate image and the observed sample. Finally, this observation model is integrated with a stochastic affine motion model to form a particle filter framework for visual tracking. Furthermore, a dynamic and robust template update strategy is applied to adapt the appearance variations of the target and reduce the possibility of drifting. Quantitative evaluations on challenging benchmark video sequences demonstrate that the proposed method is effective and can perform favorably compared to several state-of-the-art methods.

© 2015 SPIE and IS&T

Citation

Wenhui Dong ; Faliang Chang and Zijian Zhao
"Visual tracking with multifeature joint sparse representation", J. Electron. Imaging. 24(1), 013006 (Jan 07, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.1.013006


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