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Visual Tracking via Sparse and Local Linear Coding
Wang, Guofeng1,2; Qin, Xueying1,2; Zhong, Fan1,2; Liu, Yue3; Li, Hongbo3; Peng, Qunsheng4; Yang, Ming-Hsuan5
AbstractThe 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.
KeywordState space search convex sparse coding locality-constrained linear coding visual tracking
Funding Project973 Program of China[2015CB352502] ; 863 Program of China[2015AA016405] ; National Natural Science Foundation of China[61173070] ; National Natural Science Foundation of China[61202149]
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000358923100002
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Document Type期刊论文
Corresponding AuthorQin, Xueying
Affiliation1.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
Recommended Citation
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.
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