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

文献类型：期刊论文

作者Hu, Weiming1; Li, Xi1; Tian, Guodong1; Maybank, Stephen2; Zhang, Zhongfei3
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2013-05-01
卷号35期号:5页码:1051-1065
关键词Trajectory clustering and modeling incremental clustering Dirichlet process mixture model time-sensitive Dirichlet process mixture model video retrieval
英文摘要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.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]Computer Science ; Engineering
关键词[WOS]TIME-SERIES DATA ; VIDEO RETRIEVAL ; REPRESENTATION ; SURVEILLANCE ; PATTERNS
收录类别SCI
语种英语
WOS记录号WOS:000316126800003
源URL[http://ir.ia.ac.cn/handle/173211/3274]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
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
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.

入库方式： OAI收割

来源：自动化研究所

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