Title of Paper:Visual tracking via probabilistic collaborative representation

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Affiliation of Author(s):民航学院

Journal:JOURNAL OF ELECTRONIC IMAGING

Key Words:visual tracking sparse representation probabilistic collaborative representation principal component analysis squared templates

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. (C) 2017 SPIE and IS&T

ISSN No.:1017-9909

Translation or Not:no

Date of Publication:2017-01-01

Co-author:Wang, Haijun,Zhang, Shengyan,Du, Yujie

Correspondence Author:Ge Hongjuan