Knowledge Commons of Institute of Automation，CAS
| 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
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| 发表期刊 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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| 2016-10-01 | |
| 卷号 | 38期号:10页码:1983-1996 |
| 文章类型 | Article |
| 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. |
| 关键词 | Multi-object Tracking Tracklet Hierarchical Undirected Affinity Hypergraph Dense Structures |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TPAMI.2015.2509979 |
| 关键词[WOS] | MULTIPLE-TARGET TRACKING ; ROBUST FACE TRACKING ; MULTITARGET TRACKING ; APPEARANCE MODELS ; CRF MODEL ; GRAPH ; FRAMEWORK ; LINKING ; SCENES |
| 收录类别 | SCI |
| 语种 | 英语 |
| 项目资助者 | 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) |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000384240600005 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/12658 |
| 专题 | 多模态人工智能系统全国重点实验室_生物识别与安全技术 |
| 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 |
| 第一作者单位 | 模式识别国家重点实验室 |
| 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. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Lywen-Hypergraph-TPA（739KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 | |
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