Robust Visual Tracking Via Consistent Low-Rank Sparse Learning
Zhang, Tianzhu1,2; Liu, Si3; Ahuja, Narendra4; Yang, Ming-Hsuan5; Ghanem, Bernard2,6; Si Liu
2015
发表期刊International Journal of Computer Vision
卷号111期号:2页码:171-190
文章类型期刊
摘要Object 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.
关键词Visual Tracking Temporal Consistency Sparse Representation Low-rank Representation
WOS标题词Science & Technology ; Technology
学科领域Computer Science
DOI10.1007/s11263-014-0738-0
关键词[WOS]OBJECT TRACKING ; REPRESENTATION
URL查看原文
收录类别SCI
所属项目编号1149783
语种英语
资助项目NSF 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研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000348345500003
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/13642
专题模式识别国家重点实验室_多媒体计算与图形学
通讯作者Si Liu
作者单位1.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
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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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