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所属单位：民航学院
发表刊物：JOURNAL OF ELECTRONIC IMAGING
关键字：visual tracking sparse representation probabilistic collaborative representation principal component analysis squared templates
摘要：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号：1017-9909
是否译文：否
发表时间：2017-01-01
合写作者：Wang, Haijun,Zhang, Shengyan,Du, Yujie
通讯作者：葛红娟