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Visual tracking via probabilistic collaborative representation

[+] Author Affiliations
Haijun Wang

Nanjing University of Aeronautics and Astronautics, College of Civil Aviation, Nanjing, China

Binzhou University, Aviation Information Technology Research and Development, Binzhou, China

Shengyan Zhang, Yujie Du, Bo Hu

Binzhou University, Aviation Information Technology Research and Development, Binzhou, China

Hongjuan Ge

Nanjing University of Aeronautics and Astronautics, College of Civil Aviation, Nanjing, China

J. Electron. Imaging. 26(1), 013010 (Feb 01, 2017). doi:10.1117/1.JEI.26.1.013010
History: Received November 11, 2016; Accepted January 12, 2017
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Abstract.  We present a probabilistic collaborative representation method under Bayesian framework for visual tracking. First, principal component analysis (PCA) basis vectors and squared templates are used to model the appearance of tracked object. Second, to decline the high complexity in traditional tracking methods via sparse representation, we demonstrate the mechanism of a probabilistic collaborative representation method and propose a fast method for computing the coefficients. Third, we introduce a PCA basis vectors update mechanism for the appearance change of the tracked object. Experiments on challenging videos demonstrate that our method can achieve better tracking results in terms of lower center location error and higher overlap rate.

© 2017 SPIE and IS&T

Citation

Haijun Wang ; Shengyan Zhang ; Yujie Du ; Hongjuan Ge and Bo Hu
"Visual tracking via probabilistic collaborative representation", J. Electron. Imaging. 26(1), 013010 (Feb 01, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.1.013010


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