CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Robust Structural Sparse Tracking
Zhang, Tianzhu1,2; Xu, Changsheng1,2; Yang, Ming-Hsuan3
Source PublicationIEEE Transactions on Pattern Analysis and Machine Intelligence
2018-01
IssueppPages:1-1
AbstractSparse representations have been applied to visual tracking by finding the best candidate region with minimal reconstruction error based on a set of target templates. However, most existing sparse trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidate regions, thereby making them less effective when similar objects appear at close proximity or under occlusion. In this paper, we propose a novel structural sparse representation, which not only exploits the intrinsic relationships among target candidate regions and local patches to learn their representations jointly, but also preserves the spatial structure among the local patches inside each target candidate region. For robust visual tracking, we take outliers resulting from occlusion and noise into account when searching for the best target region. Constructed within a Bayesian filtering framework, we show that the proposed algorithm accommodates most existing sparse trackers with respective merits. The formulated problem can be efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. Qualitative and quantitative evaluations on challenging benchmark datasets demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.
KeywordVisual Tracking Sparse Tracking Structural Modeling Sparse Representation
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20470
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.National Lab of Pattern Recognition, Institute of Automation, CAS
2.University of Chinese Academy of Sciences
3.EECS, University of California at Merced, Merced, California United States 95344
Recommended Citation
GB/T 7714
Zhang, Tianzhu,Xu, Changsheng,Yang, Ming-Hsuan. Robust Structural Sparse Tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2018(pp):1-1.
APA Zhang, Tianzhu,Xu, Changsheng,&Yang, Ming-Hsuan.(2018).Robust Structural Sparse Tracking.IEEE Transactions on Pattern Analysis and Machine Intelligence(pp),1-1.
MLA Zhang, Tianzhu,et al."Robust Structural Sparse Tracking".IEEE Transactions on Pattern Analysis and Machine Intelligence .pp(2018):1-1.
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