Iterative Graph Seeking for Object Tracking
文献类型:期刊论文
作者 | Du, Dawei1,2; Wen, Longyin3; Qi, Honggang1,2; Huang, Qingming1,2,4; Tian, Qi5; Lyu, Siwei6 |
刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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出版日期 | 2018-04-01 |
卷号 | 27期号:4页码:1809-1821 |
关键词 | Object tracking iterative graph seeking alternative iteration scheme energy minimization |
ISSN号 | 1057-7149 |
DOI | 10.1109/TIP.2017.2785626 |
英文摘要 | 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. |
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 |
语种 | 英语 |
WOS记录号 | WOS:000429464100002 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
源URL | [http://119.78.100.204/handle/2XEOYT63/5762] ![]() |
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. |
入库方式: OAI收割
来源:计算技术研究所
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