Visual tracking via probabilistic collaborative representation

Haijun Wang; Shengyan Zhang; Yujie Du; Hongjuan Ge; Bo Hu

doi:10.1117/1.JEI.26.1.013010

1 February 2017 Visual tracking via probabilistic collaborative representation

Haijun Wang, Shengyan Zhang, Yujie Du, Hongjuan Ge, Bo Hu

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 1, 013010 (February 2017). https://doi.org/10.1117/1.JEI.26.1.013010

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.

Citation Download Citation

Haijun Wang, Shengyan Zhang, Yujie Du, Hongjuan Ge, and Bo Hu "Visual tracking via probabilistic collaborative representation," Journal of Electronic Imaging 26(1), 013010 (1 February 2017). https://doi.org/10.1117/1.JEI.26.1.013010

Received: 11 November 2016; Accepted: 12 January 2017; Published: 1 February 2017

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