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Journal of Electronic Imaging

Visual tracking via probabilistic collaborative representation
Author(s): Haijun Wang; Shengyan Zhang; Yujie Du; Hongjuan Ge; Bo Hu
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Paper 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.

Paper Details

Date Published: 1 February 2017
PDF: 17 pages
J. Electron. Imag. 26(1) 013010 doi: 10.1117/1.JEI.26.1.013010
Published in: Journal of Electronic Imaging Volume 26, Issue 1
Show Author Affiliations
Haijun Wang, Nanjing Univ. of Aeronautics and Astronautics (China)
Binzhou Univ. (China)
Shengyan Zhang, Binzhou Univ. (China)
Yujie Du, Binzhou Univ. (China)
Hongjuan Ge, Nanjing Univ. of Aeronautics and Astronautics (China)
Bo Hu, Binzhou Univ. (China)


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