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Robust Visual Tracking Via Consistent Low-Rank Sparse Learning
Zhang, Tianzhu1,2; Liu, Si3; Ahuja, Narendra4; Yang, Ming-Hsuan5; Ghanem, Bernard2,6; Si Liu
Source PublicationInternational Journal of Computer Vision
AbstractObject tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against 14 state-of-the-art tracking methods on a set of 25 challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time.
KeywordVisual Tracking Temporal Consistency Sparse Representation Low-rank Representation
WOS HeadingsScience & Technology ; Technology
Subject AreaComputer Science
Indexed BySCI
Project Number1149783
Funding ProjectNSF CAREER Grant ; research grant for the Human Sixth Sense Programme at the Advanced Digital Sciences Center from Singapore's Agency for Science, Technology and Research (A*STAR)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000348345500003
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Cited Times:167[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorSi Liu
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.Advanced Digital Sciences Center, Singapore
3.National University of Singapore
4.University of Illinois at Urbana-Champaign
5.University of California Merced
6.King Abdullah University of Science and Technology
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang, Tianzhu,Liu, Si,Ahuja, Narendra,et al. Robust Visual Tracking Via Consistent Low-Rank Sparse Learning[J]. International Journal of Computer Vision,2015,111(2):171-190.
APA Zhang, Tianzhu,Liu, Si,Ahuja, Narendra,Yang, Ming-Hsuan,Ghanem, Bernard,&Si Liu.(2015).Robust Visual Tracking Via Consistent Low-Rank Sparse Learning.International Journal of Computer Vision,111(2),171-190.
MLA Zhang, Tianzhu,et al."Robust Visual Tracking Via Consistent Low-Rank Sparse Learning".International Journal of Computer Vision 111.2(2015):171-190.
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