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Learning A Superpixel-Driven Speed Function for Level Set Tracking
Zhou, Xue1; Li, Xi2; Hu, Weiming3
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
2016-07-01
Volume46Issue:7Pages:1498-1510
SubtypeArticle
AbstractA key problem in level set tracking is to construct a discriminative speed function for effective contour evolution. In this paper, we propose a level set tracking method based on a discriminative speed function, which produces a superpixel-driven force for effective level set evolution. Based on kernel density estimation and metric learning, the speed function is capable of effectively encoding the discriminative information on object appearance within a feasible metric space. Furthermore, we introduce adaptive object shape modeling into the level set evolution process, which leads to the tracking robustness in complex scenarios. To ensure the efficiency of adaptive object shape modeling, we develop a simple but efficient weighted non-negative matrix factorization method that can online learn an object shape dictionary. Experimental results on a number of challenging video sequences demonstrate the effectiveness and robustness of the proposed tracking method.
KeywordLevel Set Tracking Metric Learning Non-negative Matrix Factorization (Nf) Speed Function Superpixel (Sp)-driven
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TCYB.2015.2451100
WOS KeywordGEODESIC ACTIVE CONTOURS ; IMAGE SEGMENTATION ; MATRIX FACTORIZATION ; VISUAL TRACKING ; OBJECT TRACKING ; SHAPE PRIORS
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(NSFC 61472063 ; 973 Basic Research Program of China(2014CB349303) ; Fundamental Research Funds for the Central Universities(ZYGX 2011J076) ; China Knowledge Centre for Engineering Sciences and Technology ; National Basic Research Program of China(2012CB316400) ; Zhejiang Provincial Engineering Center on Media Data Cloud Processing and Analysis ; NVIDIA CUDA Research Center Program ; Microsoft Research Asia Collaborative Research Program ; MOE-Microsoft Key Laboratory, Zhejiang University ; 61472353)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000379757900002
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12153
Collection模式识别国家重点实验室_视频内容安全
Affiliation1.Univ Elect Sci & Technol China, Dept Automat Engn, Chengdu 611731, Peoples R China
2.Zhejiang Univ, Coll Comp Sci, Hangzhou 310027, Zhejiang, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhou, Xue,Li, Xi,Hu, Weiming. Learning A Superpixel-Driven Speed Function for Level Set Tracking[J]. IEEE TRANSACTIONS ON CYBERNETICS,2016,46(7):1498-1510.
APA Zhou, Xue,Li, Xi,&Hu, Weiming.(2016).Learning A Superpixel-Driven Speed Function for Level Set Tracking.IEEE TRANSACTIONS ON CYBERNETICS,46(7),1498-1510.
MLA Zhou, Xue,et al."Learning A Superpixel-Driven Speed Function for Level Set Tracking".IEEE TRANSACTIONS ON CYBERNETICS 46.7(2016):1498-1510.
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