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Exploiting Hierarchical Dense Structures on Hypergraphs for Multi-Object Tracking
Wen, Longyin1,2; Lei, Zhen1,2; Lyu, Siwei3; Li, Stan Z.1,2; Yang, Ming-Hsuan4
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2016-10-01
Volume38Issue:10Pages:1983-1996
SubtypeArticle
AbstractMost 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.
KeywordMulti-object Tracking Tracklet Hierarchical Undirected Affinity Hypergraph Dense Structures
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TPAMI.2015.2509979
WOS KeywordMULTIPLE-TARGET TRACKING ; ROBUST FACE TRACKING ; MULTITARGET TRACKING ; APPEARANCE MODELS ; CRF MODEL ; GRAPH ; FRAMEWORK ; LINKING ; SCENES
Indexed BySCI
Language英语
Funding OrganizationNational 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)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000384240600005
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12658
Collection模式识别国家重点实验室_生物识别与安全技术研究
Affiliation1.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
Recommended Citation
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.
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