Computer Science ›› 2018, Vol. 45 ›› Issue (3): 69-75.doi: 10.11896/j.issn.1002-137X.2018.03.012

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Discriminative Visual Tracking by Collaborative Structural Sparse Reconstruction

YOU Si-si, YING Long, GUO Wen, DING Xin-miao and HUA Zhen   

  • Online:2018-03-15 Published:2018-11-13

Abstract: Though the appearance likelihood model based on sparse representation has been widely applied in visual tracking,the single generation object representation model can easily be interrupted by background clutter due to not considering the full discriminative structural information.In order to alleviate the drift problem of the visual tracking,this paper presented a novel tracking method based on collaborative sparse reconstruction of object appearance dictionary and background dictionary.This paper achieved a more accurate description of the target appearance model by constructing a discriminative appearance likelihood model based on sparse representation.Then,it embedded discriminative information into the appearance likelihood model by a reasonable method of selecting the sparse coefficients of candidate target region and candidate background region.By that way,it can explore the potential correlation of candidate target region and the structure relation of candidate background region,so as to learn the appearance model of candidate target area more accurately.Many experimental results in challenging sequence verify the robustness of this method.The proposed tracker outperforms excellent performance in comparison with other state-of-the-art trackers.

Key words: Collaborative sparse reconstruction,Sparse representation,Graphic methods,Discriminative tracking

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