An Incremental DPMM-Based Method for Trajectory Clustering, Modeling, and Retrieval

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	An Incremental DPMM-Based Method for Trajectory Clustering, Modeling, and Retrieval
	Hu, Weiming1 ; Li, Xi 1; Tian, Guodong 1; Maybank, Stephen 2; Zhang, Zhongfei 3
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2013-05-01
卷号	35 期号:5 页码:1051-1065
文章类型	Article
摘要	Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.
关键词	Trajectory Clustering And Modeling Incremental Clustering Dirichlet Process Mixture Model Time-sensitive Dirichlet Process Mixture Model Video Retrieval
WOS标题词	Science & Technology ; Technology
关键词[WOS]	TIME-SERIES DATA ; VIDEO RETRIEVAL ; REPRESENTATION ; SURVEILLANCE ; PATTERNS
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000316126800003
引用统计	被引频次：84[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3274
专题	多模态人工智能系统全国重点实验室_视频内容安全
作者单位	1.Chinese Acad Sci, NLPR, Inst Automat, Beijing 100190, Peoples R China 2.Univ London Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HX, England 3.SUNY Binghamton, Dept Comp Sci, Watson Sch Engn & Appl Sci, Binghamton, NY 13902 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	Hu, Weiming,Li, Xi,Tian, Guodong,et al. An Incremental DPMM-Based Method for Trajectory Clustering, Modeling, and Retrieval[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2013,35(5):1051-1065.
APA	Hu, Weiming,Li, Xi,Tian, Guodong,Maybank, Stephen,&Zhang, Zhongfei.(2013).An Incremental DPMM-Based Method for Trajectory Clustering, Modeling, and Retrieval.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,35(5),1051-1065.
MLA	Hu, Weiming,et al."An Incremental DPMM-Based Method for Trajectory Clustering, Modeling, and Retrieval".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 35.5(2013):1051-1065.