Knowledge Commons of Institute of Automation,CAS
Rank-1 Tensor Approximation for High-Order Association in Multi-target Tracking | |
Shi, Xinchu1; Ling, Haibin4; Pang, Yu4; Hu, Weiming2,3; Chu, Peng4; Xing, Junliang1 | |
发表期刊 | INTERNATIONAL JOURNAL OF COMPUTER VISION |
ISSN | 0920-5691 |
2019-08-01 | |
卷号 | 127期号:8页码:1063-1083 |
通讯作者 | Hu, Weiming(wmhu@nlpr.ia.ac.cn) |
摘要 | High-order motion information is important in multi-target tracking (MTT) especially when dealing with large inter-target ambiguities. Such high-order information can be naturally modeled as a multi-dimensional assignment (MDA) problem, whose global solution is however intractable in general. In this paper, we propose a novel framework to the problem by reshaping MTT as a rank-1 tensor approximation problem (R1TA). We first show that MDA and R1TA share the same objective function and similar constraints. This discovery opens a door to use high-order tensor analysis for MTT and suggests the exploration of R1TA. In particular, we develop a tensor power iteration algorithm to effectively capture high-order motion information as well as appearance variation. The proposed algorithm is evaluated on a diverse set of datasets including aerial video sequences containing ariel borne dense highway scenes, top-view pedestrian trajectories, multiple similar objects, normal view pedestrians and vehicles. The effectiveness of the proposed algorithm is clearly demonstrated in these experiments. |
关键词 | Multi-target tracking Multi-dimensional assignment Rank-1 tensor approximation Data association |
DOI | 10.1007/s11263-018-01147-z |
关键词[WOS] | ALGORITHM |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Beijing Natural Science Foundation[L172051] ; Natural Science Foundation of China[61502492] ; Natural Science Foundation of China[61751212] ; Natural Science Foundation of China[61721004] ; NSFC-general technology collaborative Fund for basic research[U1636218] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC040] ; CAS External Cooperation Key Project ; US NSF[1814745] ; US NSF[1407156] ; US NSF[1350521] ; Beijing Natural Science Foundation[L172051] ; Natural Science Foundation of China[61502492] ; Natural Science Foundation of China[61751212] ; Natural Science Foundation of China[61721004] ; NSFC-general technology collaborative Fund for basic research[U1636218] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC040] ; CAS External cooperation key project ; US NSF[1814745] ; US NSF[1407156] ; US NSF[1350521] |
项目资助者 | Beijing Natural Science Foundation ; Natural Science Foundation of China ; NSFC-general technology collaborative Fund for basic research ; Key Research Program of Frontier Sciences, CAS ; CAS External cooperation key project ; US NSF |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000474559000006 |
出版者 | SPRINGER |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/26850 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
通讯作者 | Hu, Weiming |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Shi, Xinchu,Ling, Haibin,Pang, Yu,et al. Rank-1 Tensor Approximation for High-Order Association in Multi-target Tracking[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2019,127(8):1063-1083. |
APA | Shi, Xinchu,Ling, Haibin,Pang, Yu,Hu, Weiming,Chu, Peng,&Xing, Junliang.(2019).Rank-1 Tensor Approximation for High-Order Association in Multi-target Tracking.INTERNATIONAL JOURNAL OF COMPUTER VISION,127(8),1063-1083. |
MLA | Shi, Xinchu,et al."Rank-1 Tensor Approximation for High-Order Association in Multi-target Tracking".INTERNATIONAL JOURNAL OF COMPUTER VISION 127.8(2019):1063-1083. |
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