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Collaborating visual tracker based on particle filter and correlation filter

文献类型：期刊论文

作者Li, Weiguang1,3; Wei, Wang2; Qiang, Han3; Shi, Mingquan1
刊名CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
出版日期2019-06-25
卷号31期号:12页码:13
ISSN号1532-0626
关键词Bayesian estimation computer vision correlation filter particle filter
DOI10.1002/cpe.4665
通讯作者Shi, Mingquan(shi_mingquan@sina.com)
英文摘要Correlation filter (CF)-based tracking algorithms is most popular in recent years due to its high accuracy and impressive speed. However, it has some intrinsically drawbacks such as margin suppression, sensitive for disturbance, and partial occlusions. Contrasted with CF drawbacks, the advantages of particle filter (PF) tracking algorithm include robustness, motion prediction, and wide detection range. Therefore, it can amend some CF tracker drawbacks. On the other hand, the HOG feature is widely used in CF tracker because it can detect the target precision position. However, this kind of feature is rotation-variation, which is invalid for rotation transformation target. On the contrary, the tracker precision merely based on colour feature is rough, but colour feature is rotation invariation and is effective for rotating target; therefore, these two features are complementary. In this paper, we integrate both trackers (CF and PF) to learn the HOG and colour feature, respectively, experiments demonstrate this tracking algorithm is more robust, and the tracking precision is more accurate. This algorithm is integrated with some classic CF trackers (KCF, SAMF, and MOSSE) framework and benchmark them against their baseline. On the OTB2015 benchmark datasets, experiment result demonstrates OPE performance grades have improved from about 1% to 12%; SRE Performance grades have improved from about 1.3% to 5.8%.
资助项目National Natural Science Foundation of China[61605205]
WOS研究方向Computer Science
语种英语
出版者WILEY
WOS记录号WOS:000468975000014
源URL[http://119.78.100.138/handle/2HOD01W0/8130]  
专题智能工业设计工程中心
通讯作者Shi, Mingquan
作者单位1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Sichuan Univ Sci & Engn, Artificial Intelligence Key Lab, Zigong 643000, Peoples R China
推荐引用方式
GB/T 7714		 Li, Weiguang,Wei, Wang,Qiang, Han,et al. Collaborating visual tracker based on particle filter and correlation filter[J]. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE,2019,31(12):13.
APA Li, Weiguang,Wei, Wang,Qiang, Han,&Shi, Mingquan.(2019).Collaborating visual tracker based on particle filter and correlation filter.CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE,31(12),13.
MLA Li, Weiguang,et al."Collaborating visual tracker based on particle filter and correlation filter".CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 31.12(2019):13.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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