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论文类型：期刊论文

发表时间：2015-07-01

发表刊物：IEEE SIGNAL PROCESSING LETTERS

收录刊物：SCIE、EI、Scopus

卷号：22

期号：7

页面范围：794-798

ISSN号：1070-9908

关键字：Sparse representation; structural grouping; visual tracking

摘要：This letter introduces a novel two-pass structural grouping algorithm and casts visual tracking as foreground superpixels grouping problem. In the first step, pairwise superpixel grouping is conducted in four orientations. Grouping prototypes containing the prior information of foreground and background are generated to determine whether any pair of neighboring superpixels should be grouped together. In the second step, superpixels selected by the first step are grouped into a single region which serves as the object region. The proposed grouping method has two benefits over the state-of-the-art ones. First, pairwise grouping is independently conducted in four orientations, which exploits the local structure of the foregound/backgroud and facilitates a more robust grouping process. Second, rather than considering the similarity of two neighboring superpixels, the grouping process is performed via accounting for the prior information of the object and the background, which is more suitable for visual tracking. Many experiments on challenging video clips demonstrate that our method achieves good performance than the state-of-the-art trackers in a wide range of tracking scenarios.