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Visual Tracking via Structure Constrained Grouping
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DOI：10.1109/LSP.2014.2369476.png)
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摘要
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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.
Keyword:
Sparse representation
structural grouping
visual tracking
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期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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