Collaborating visual tracker based on particle filter and correlation filter

doi:10.1002/cpe.4665

	Collaborating visual tracker based on particle filter and correlation filter
	Li, Weiguang 1,3; Wei, Wang 2; Qiang, Han 3; Shi, Mingquan1
刊名	CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
	2019-06-25
卷号	31 期号:12 页码:13
关键词	Bayesian estimation computer vision correlation filter particle filter
ISSN号	1532-0626
DOI	10.1002/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
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/8130]
专题	中国科学院重庆绿色智能技术研究院
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.