CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Correlation Particle Filter for Visual Tracking
Zhang, Tianzhu1,2; Liu, Si3; Xu, Changsheng1,2; Liu, Bin4; Yang, Ming-Hsuan5
AbstractIn this paper, we propose a novel correlation particle filter (CPF) for robust visual tracking. Instead of a simple combination of a correlation filter and a particle filter, we exploit and complement the strength of each one. Compared with existing tracking methods based on correlation filters and particle filters, the proposed tracker has four major advantages: 1) it is robust to partial and total occlusions, and can recover from lost tracks by maintaining multiple hypotheses; 2) it can effectively handle large-scale variation via a particle sampling strategy; 3) it can efficiently maintain multiple modes in the posterior density using fewer particles than conventional particle filters, resulting in low computational cost; and 4) it can shepherd the sampled particles toward the modes of the target state distribution using a mixture of correlation filters, resulting in robust tracking performance. Extensive experimental results on challenging benchmark data sets demonstrate that the proposed CPF tracking algorithm performs favorably against the state-of-the-art methods.
KeywordVisual Tracking Correlation Filter Particle Filter
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(61432019 ; Beijing Natural Science Foundation(4172062) ; Key Research Program of Frontier Sciences, CAS(QYZDJ-SSW-JSC039) ; 61572498 ; 61532009 ; 61572493 ; U1536203)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000427637600006
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Cited Times:33[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Beihang Univ, Beijing Key Lab Digital Media, Sch Comp Sci & Engn, Beijing 100191, Peoples R China
4.Moshanghua Tech Co Ltd, Beijing 100081, Peoples R China
5.Univ Calif Merced, Sch Engn, Merced, CA 95344 USA
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Tianzhu,Liu, Si,Xu, Changsheng,et al. Correlation Particle Filter for Visual Tracking[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2018,27(6):2676-2687.
APA Zhang, Tianzhu,Liu, Si,Xu, Changsheng,Liu, Bin,&Yang, Ming-Hsuan.(2018).Correlation Particle Filter for Visual Tracking.IEEE TRANSACTIONS ON IMAGE PROCESSING,27(6),2676-2687.
MLA Zhang, Tianzhu,et al."Correlation Particle Filter for Visual Tracking".IEEE TRANSACTIONS ON IMAGE PROCESSING 27.6(2018):2676-2687.
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