Knowledge Commons of Institute of Automation,CAS
Learning Multi-task Correlation Particle Filters for Visual Tracking | |
Zhang, Tianzhu1,2; Xu, Changsheng1,2; Yang, Ming-Hsuan3 | |
发表期刊 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
2018-01 | |
期号 | pp页码:1-1 |
摘要 | 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 |
关键词 | Visual Tracking Correlation Filter Structural Modeling Particle Filter |
WOS记录号 | WOS:000456150600008 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/20472 |
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.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 |
推荐引用方式 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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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
pami17_mcpf_final.pd(21435KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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