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论文类型：期刊论文

发表时间：2016-09-01

发表刊物：IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY

收录刊物：SCIE、EI、Scopus

卷号：26

期号：9

页面范围：1709-1721

ISSN号：1051-8215

关键字：Gaussian-Laplacian noise; least soft-threshold squares (LSS); linear representation; visual tracking

摘要：In this paper, we propose an online tracking algorithm based on a novel robust linear regression estimator. In contrast to existing methods, the proposed least soft-threshold squares (LSS) algorithm models the error term with the Gaussian-Laplacian distribution, which can be efficiently solved. For visual tracking, the Gaussian-Laplacian noise assumption enables our LSS model to handle the normal appearance change and outlier simultaneously. Based on the maximum joint likelihood of parameters, we derive an LSS distance metric to measure the difference between an observation sample and a dictionary of positive templates. Compared with the distance derived from ordinary least squares methods, the proposed metric is more effective in dealing with the outliers. In addition, we provide insights on the relationships among the LSS problem, Huber loss function, and trivial templates, which facilitate better understandings of the existing tracking methods. Finally, we develop a robust tracking algorithm based on the LSS distance metric with an update scheme and negative templates, and speed it up with a particle selection mechanism. Experimental results on numerous challenging image sequences demonstrate that the proposed tracking algorithm performs favorably than the state-of-the-art methods.