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Exploiting Hierarchical Dense Structures on Hypergraphs for Multi-Object Tracking

文献类型：期刊论文

作者Wen, Longyin1,2; Lei, Zhen1,2; Lyu, Siwei3; Li, Stan Z.1,2; Yang, Ming-Hsuan4
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2016-10-01
卷号38期号:10页码:1983-1996
关键词Multi-object Tracking Tracklet Hierarchical Undirected Affinity Hypergraph Dense Structures
DOI10.1109/TPAMI.2015.2509979
文献子类Article
英文摘要Most multi-object tracking algorithms are developed within the tracking-by-detection framework that consider the pairwise appearance similarities between detection responses or tracklets within a limited temporal window, and thus less effective in handling long-term occlusions or distinguishing spatially close targets with similar appearance in crowded scenes. In this work, we propose an algorithm that formulates the multi-object tracking task as one to exploit hierarchical dense structures on an undirected hypergraph constructed based on tracklet affinity. The dense structures indicate a group of vertices that are inter-connected with a set of hyperedges with high affinity values. The appearance and motion similarities among multiple tracklets across the spatio-temporal domain are considered globally by exploiting high-order similarities rather than pairwise ones, thereby facilitating distinguish spatially close targets with similar appearance. In addition, the hierarchical design of the optimization process helps the proposed tracking algorithm handle long-term occlusions robustly. Extensive experiments on various challenging datasets of both multi-pedestrian and multi-face tracking tasks, demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
WOS关键词MULTIPLE-TARGET TRACKING ; ROBUST FACE TRACKING ; MULTITARGET TRACKING ; APPEARANCE MODELS ; CRF MODEL ; GRAPH ; FRAMEWORK ; LINKING ; SCENES
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000384240600005
资助机构National Natural Science Foundation of China(61375037 ; National Science and Technology Support Program Project(2013BAK02B01) ; Chinese Academy of Sciences Project(KGZD-EW-102-2) ; AuthenMetric RD Funds ; US NSF(IIS-0953373 ; NSF(1149783) ; NSF IIS Grant(1152576) ; 61473291 ; CCF-1319800) ; 61572501 ; 61572536)
源URL[http://ir.ia.ac.cn/handle/173211/12658]  
专题自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
作者单位1.Chinese Acad Sci, Ctr Biometr & Secur Res, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
3.SUNY Albany, Dept Comp Sci, Albany, GA USA
4.Univ Calif Merced, Sch Engn, Merced, CA USA
推荐引用方式
GB/T 7714		 Wen, Longyin,Lei, Zhen,Lyu, Siwei,et al. Exploiting Hierarchical Dense Structures on Hypergraphs for Multi-Object Tracking[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2016,38(10):1983-1996.
APA Wen, Longyin,Lei, Zhen,Lyu, Siwei,Li, Stan Z.,&Yang, Ming-Hsuan.(2016).Exploiting Hierarchical Dense Structures on Hypergraphs for Multi-Object Tracking.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,38(10),1983-1996.
MLA Wen, Longyin,et al."Exploiting Hierarchical Dense Structures on Hypergraphs for Multi-Object Tracking".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 38.10(2016):1983-1996.

入库方式： OAI收割

来源：自动化研究所

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