Robust Visual Tracking Via Consistent Low-Rank Sparse Learning

doi:10.1007/s11263-014-0738-0

	Robust Visual Tracking Via Consistent Low-Rank Sparse Learning
	Zhang, Tianzhu1,2 ; Liu, Si 3; Ahuja, Narendra 4; Yang, Ming-Hsuan 5; Ghanem, Bernard 2,6; Si Liu
发表期刊	International Journal of Computer Vision
	2015
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
DOI	10.1007/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	被引频次：229[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13642
专题	多模态人工智能系统全国重点实验室_多媒体计算
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
第一作者单位	中国科学院自动化研究所
推荐引用方式 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.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
ConsistentLowRankTra（3975KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载