Iterative Graph Seeking for Object Tracking

doi:10.1109/TIP.2017.2785626

	Iterative Graph Seeking for Object Tracking
	Du, Dawei 1,2; Wen, Longyin 3; Qi, Honggang 1,2; Huang, Qingming 1,2,4; Tian, Qi 5; Lyu, Siwei 6
	2018-04-01
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	27 期号:4 页码:1809-1821
摘要	To effectively solve the challenges in object tracking, such as large deformation and severe occlusion, many existing methods use graph-based models to capture target part relations, and adopt a sequential scheme of target part selection, part matching, and state estimation. However, such methods have two major drawbacks: 1) inaccurate part selection leads to performance deterioration of part matching and state estimation and 2) there are insufficient effective global constraints for local part selection and matching. In this paper, we propose a new object tracking method based on iterative graph seeking, which integrate target part selection, part matching, and state estimation using a unified energy minimization framework. Our method also incorporates structural information in local parts variations using the global constraint. We devise an alternative iteration scheme to minimize the energy function for searching the most plausible target geometric graph. Experimental results on several challenging benchmarks (i.e., VOT2015, OTB2013, and OTB2015) demonstrate improved performance and robustness in comparison with existing algorithms.
关键词	Object tracking iterative graph seeking alternative iteration scheme energy minimization
DOI	10.1109/TIP.2017.2785626
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[61650202] ; National Natural Science Foundation of China[61472388] ; National Natural Science Foundation of China[61771341] ; National Natural Science Foundation of China[61429201] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-SYS013] ; ARO[W911NF-15-1-0290] ; Faculty Research Gift Awards through the NEC Laboratories of America and Blippar ; U.S. National Science Foundation National Robotics Initiative[IIS-1537023]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000429464100002
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：19[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/5763
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Qi, Honggang; Huang, Qingming
作者单位	1.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 101408, Peoples R China 3.GE Global Res, Niskayuna, NY 12309 USA 4.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China 5.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA 6.SUNY Albany, Dept Comp Sci, Albany, NY 12222 USA
推荐引用方式 GB/T 7714	Du, Dawei,Wen, Longyin,Qi, Honggang,et al. Iterative Graph Seeking for Object Tracking[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2018,27(4):1809-1821.
APA	Du, Dawei,Wen, Longyin,Qi, Honggang,Huang, Qingming,Tian, Qi,&Lyu, Siwei.(2018).Iterative Graph Seeking for Object Tracking.IEEE TRANSACTIONS ON IMAGE PROCESSING,27(4),1809-1821.
MLA	Du, Dawei,et al."Iterative Graph Seeking for Object Tracking".IEEE TRANSACTIONS ON IMAGE PROCESSING 27.4(2018):1809-1821.