Correlation Particle Filter for Visual Tracking | |
Zhang, Tianzhu1,2; Liu, Si3; Xu, Changsheng1,2; Liu, Bin4; Yang, Ming-Hsuan5 | |
刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
2018-06-01 | |
卷号 | 27期号:6页码:2676-2687 |
关键词 | Visual Tracking Correlation Filter Particle Filter |
DOI | 10.1109/TIP.2017.2781304 |
文献子类 | Article |
英文摘要 | In 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. |
WOS关键词 | OBJECT TRACKING ; EIGENTRACKING ; MODELS |
WOS研究方向 | Computer Science ; Engineering |
语种 | 英语 |
WOS记录号 | WOS:000427637600006 |
资助机构 | National 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) |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/20469] |
专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
作者单位 | 1.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 |
推荐引用方式 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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