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An Incremental DPMM-Based Method for Trajectory Clustering, Modeling, and Retrieval
Hu, Weiming1; Li, Xi1; Tian, Guodong1; Maybank, Stephen2; Zhang, Zhongfei3
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2013-05-01
Volume35Issue:5Pages:1051-1065
SubtypeArticle
AbstractTrajectory 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.
KeywordTrajectory Clustering And Modeling Incremental Clustering Dirichlet Process Mixture Model Time-sensitive Dirichlet Process Mixture Model Video Retrieval
WOS HeadingsScience & Technology ; Technology
WOS KeywordTIME-SERIES DATA ; VIDEO RETRIEVAL ; REPRESENTATION ; SURVEILLANCE ; PATTERNS
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000316126800003
Citation statistics
Cited Times:58[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3274
Collection模式识别国家重点实验室_视频内容安全
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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.
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