Coupling Reranking and Structured Output SVM Co-Train for Multitarget Tracking

doi:10.1109/TCSVT.2015.2433173

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Coupling Reranking and Structured Output SVM Co-Train for Multitarget Tracking
	Xu, Yingkun 1,2; Qin, Lei 1; Huang, Qingming 1,3
刊名	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
	2016-06-01
卷号	26 期号:6 页码:1084-1098
关键词	Co-train multitarget tracking reranking structured output SVM (SSVM)
ISSN号	1051-8215
DOI	10.1109/TCSVT.2015.2433173
英文摘要	Most of the previous works for multitarget tracking employ two strategies: global optimization and online state estimation. In general, global methods attempt to prevent local optimization and find the best results given global models. However, in time-critical applications, global optimization has long temporal latency. In contrast, most of the online algorithms obtain the states with greedy estimation, in which the errors are hard to be corrected. In this paper, we combine these two strategies and propose an online tracking approach with short-term storage to correct some local association errors. Based on structured output support vector machine, we propose a new framework with multiple online learners to produce multiple best local linkages, and novel features based on previously generated multiframe associations are designed for reranking of these multiple linkages. The reranking also serves as the appropriate mediator for updating of the online learners by co-train algorithm. The experimental results illustrate the advantage and robustness of this reranking algorithm, and its discrimination to find optimal ones. Comparison with some state-ofthe- art methods proves that our proposed method is competitive to global optimal ones and is superior to other online tracking algorithms.
资助项目	National Basic Research Program of China (973 Program)[2015CB351802] ; National Basic Research Program of China (973 Program)[2012CB316400] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[61025011] ; National Natural Science Foundation of China[61133003] ; National Natural Science Foundation of China[61390510]
WOS研究方向	Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000378507600006
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/8335]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xu, Yingkun
作者单位	1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China 2.Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Zhejiang, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Xu, Yingkun,Qin, Lei,Huang, Qingming. Coupling Reranking and Structured Output SVM Co-Train for Multitarget Tracking[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2016,26(6):1084-1098.
APA	Xu, Yingkun,Qin, Lei,&Huang, Qingming.(2016).Coupling Reranking and Structured Output SVM Co-Train for Multitarget Tracking.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,26(6),1084-1098.
MLA	Xu, Yingkun,et al."Coupling Reranking and Structured Output SVM Co-Train for Multitarget Tracking".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 26.6(2016):1084-1098.