Visual Tracking via Sparse and Local Linear Coding

doi:10.1109/TIP.2015.2445291

CSpace > 系统科学研究所

	Visual Tracking via Sparse and Local Linear Coding
	Wang, Guofeng 1,2; Qin, Xueying 1,2; Zhong, Fan 1,2; Liu, Yue 3; Li, Hongbo3 ; Peng, Qunsheng 4; Yang, Ming-Hsuan 5
	2015-11-01
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	24 期号:11 页码:3796-3809
摘要	The state search is an important component of any object tracking algorithm. Numerous algorithms have been proposed, but stochastic sampling methods (e.g., particle filters) are arguably one of the most effective approaches. However, the discretization of the state space complicates the search for the precise object location. In this paper, we propose a novel tracking algorithm that extends the state space of particle observations from discrete to continuous. The solution is determined accurately via iterative linear coding between two convex hulls. The algorithm is modeled by an optimal function, which can be efficiently solved by either convex sparse coding or locality constrained linear coding. The algorithm is also very flexible and can be combined with many generic object representations. Thus, we first use sparse representation to achieve an efficient searching mechanism of the algorithm and demonstrate its accuracy. Next, two other object representation models, i.e., least soft-threshold squares and adaptive structural local sparse appearance, are implemented with improved accuracy to demonstrate the flexibility of our algorithm. Qualitative and quantitative experimental results demonstrate that the proposed tracking algorithm performs favorably against the state-of-the-art methods in dynamic scenes.
关键词	State space search convex sparse coding locality-constrained linear coding visual tracking
DOI	10.1109/TIP.2015.2445291
语种	英语
资助项目	973 Program of China[2015CB352502] ; 863 Program of China[2015AA016405] ; National Natural Science Foundation of China[61173070] ; National Natural Science Foundation of China[61202149]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000358923100002
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/20477
专题	系统科学研究所
通讯作者	Qin, Xueying
作者单位	1.Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Peoples R China 2.Minist Educ, Engn Res Ctr Digital Media Technol, Jinan 250100, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 4.Zhejiang Univ, State Key Lab Comp Aided Design & Comp Graph, Hangzhou 310027, Zhejiang, Peoples R China 5.Univ Calif, Dept Elect Engn & Comp Sci, Merced, CA 95344 USA
推荐引用方式 GB/T 7714	Wang, Guofeng,Qin, Xueying,Zhong, Fan,et al. Visual Tracking via Sparse and Local Linear Coding[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2015,24(11):3796-3809.
APA	Wang, Guofeng.,Qin, Xueying.,Zhong, Fan.,Liu, Yue.,Li, Hongbo.,...&Yang, Ming-Hsuan.(2015).Visual Tracking via Sparse and Local Linear Coding.IEEE TRANSACTIONS ON IMAGE PROCESSING,24(11),3796-3809.
MLA	Wang, Guofeng,et al."Visual Tracking via Sparse and Local Linear Coding".IEEE TRANSACTIONS ON IMAGE PROCESSING 24.11(2015):3796-3809.