Correlation Particle Filter for Visual Tracking
Zhang, Tianzhu1,2; Liu, Si3; Xu, Changsheng1,2; Liu, Bin4; Yang, Ming-Hsuan5
2018-06-01
发表期刊IEEE TRANSACTIONS ON IMAGE PROCESSING
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
DOI10.1109/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:4[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
专题模式识别国家重点实验室_多媒体计算与图形学
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.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Zhang, Tianzhu]的文章
[Liu, Si]的文章
[Xu, Changsheng]的文章
百度学术
百度学术中相似的文章
[Zhang, Tianzhu]的文章
[Liu, Si]的文章
[Xu, Changsheng]的文章
必应学术
必应学术中相似的文章
[Zhang, Tianzhu]的文章
[Liu, Si]的文章
[Xu, Changsheng]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。