CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Learning Multi-task Correlation Particle Filters for Visual Tracking
Zhang, Tianzhu1,2; Xu, Changsheng1,2; Yang, Ming-Hsuan3
Source PublicationIEEE Transactions on Pattern Analysis and Machine Intelligence
2018-01
IssueppPages:1-1
Abstract

We propose a multi-task correlation particle filter (MCPF) for robust visual tracking. We first present the multi-task correlation filter (MCF) that takes the interdependencies among different object parts and features into account to learn the correlation filters jointly. The proposed MCPF is introduced to exploit and complement the strength of a MCF and a particle filter. Compared with existing tracking methods based on correlation filters and particle filters, the proposed MCPF enjoys several merits. First, it exploits the interdependencies among different features to derive the correlation filters jointly, and makes the learned filters complement and enhance each other to obtain consistent responses. Second, it handles partial occlusion via a part-based representation, and exploits the intrinsic relationship among local parts via spatial constraints to preserve object structure and learn the correlation filters jointly. Third, it effectively handles large scale variation via a sampling scheme by drawing particles at different scales for target object state estimation. Fourth, it shepherds the sampled particles toward the modes of the target state distribution via the MCF, and effectively covers object states well using fewer particles than conventional particle filters. Extensive experimental results odemonstrate that the proposed MCPF tracking algorithm performs favorably against the state-of-the-art methods

KeywordVisual Tracking Correlation Filter Structural Modeling Particle Filter
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20472
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.National Lab of Pattern Recognition, Institute of Automation, CAS
2.University of Chinese Academy of Sciences
3.EECS, University of California at Merced, Merced, California United States 95344
Recommended Citation
GB/T 7714
Zhang, Tianzhu,Xu, Changsheng,Yang, Ming-Hsuan. Learning Multi-task Correlation Particle Filters for Visual Tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2018(pp):1-1.
APA Zhang, Tianzhu,Xu, Changsheng,&Yang, Ming-Hsuan.(2018).Learning Multi-task Correlation Particle Filters for Visual Tracking.IEEE Transactions on Pattern Analysis and Machine Intelligence(pp),1-1.
MLA Zhang, Tianzhu,et al."Learning Multi-task Correlation Particle Filters for Visual Tracking".IEEE Transactions on Pattern Analysis and Machine Intelligence .pp(2018):1-1.
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