Correlation Particle Filter for Visual Tracking

doi:10.1109/TIP.2017.2781304

	Correlation Particle Filter for Visual Tracking
	Zhang, Tianzhu1,2 ; Liu, Si 3; Xu, Changsheng1,2 ; Liu, Bin 4; Yang, Ming-Hsuan 5
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2018-06-01
卷号	27 期号:6 页码:2676-2687
文章类型	Article
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.
关键词	Visual Tracking Correlation Filter Particle Filter
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TIP.2017.2781304
关键词[WOS]	OBJECT TRACKING ; EIGENTRACKING ; MODELS
收录类别	SCI
语种	英语
项目资助者	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)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000427637600006
引用统计	被引频次：100[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/20469
专题	多模态人工智能系统全国重点实验室_多媒体计算
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
第一作者单位	模式识别国家重点实验室
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.