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Dual Sticky Hierarchical Dirichlet Process Hidden Markov Model and Its Application to Natural Language Description of Motions
Hu, Weiming1; Tian, Guodong1; Kang, Yongxin1; Yuan, Chunfeng1; Maybank, Stephen2
2018-10-01
发表期刊IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷号40期号:10页码:2355-2373
文章类型Article
摘要In this paper, a new nonparametric Bayesian model called the dual sticky hierarchical Dirichlet process hidden Markov model (HDP-HMM) is proposed for mining activities from a collection of time series data such as trajectories. All the time series data are clustered. Each cluster of time series data, corresponding to a motion pattern, is modeled by an HMM. Our model postulates a set of HMMs that share a common set of states (topics in an analogy with topic models for document processing), but have unique transition distributions. The number of HMMs and the number of topics are both automatically determined. The sticky prior avoids redundant states and makes our HDP-HMM more effective to model multimodal observations. For the application to motion trajectory modeling, topics correspond to motion activities. The learnt topics are clustered into atomic activities which are assigned predicates. We propose a Bayesian inference method to decompose a given trajectory into a sequence of atomic activities. The sources and sinks in the scene are learnt by clustering endpoints (origins and destinations) of trajectories. The semantic motion regions are learnt using the points in trajectories. On combining the learnt sources and sinks, the learnt semantic motion regions, and the learnt sequence of atomic activities, the action represented by a trajectory can be described in natural language in as automatic a way as possible. The effectiveness of our dual sticky HDP-HMM is validated on several trajectory datasets. The effectiveness of the natural language descriptions for motions is demonstrated on the vehicle trajectories extracted from a traffic scene.
关键词Hdp-hmm Sticky Prior Motion Pattern Learning Natural Language Description
WOS标题词Science & Technology ; Technology
DOI10.1109/TPAMI.2017.2756039
关键词[WOS]TRAJECTORY ANALYSIS ; SAMPLING METHODS ; VIDEO RETRIEVAL ; RECOGNITION ; PATTERNS ; SYSTEM ; PRIORS
收录类别SCI
语种英语
项目资助者973 basic research program of China(2014CB349303) ; Natural Science Foundation of China(U1636218 ; Strategic Priority Research Program of the CAS(XDB02070003) ; CAS External cooperation key project ; 61472421)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000443875500006
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/21828
专题模式识别国家重点实验室_视频内容安全
作者单位1.Chinese Acad Sci, Univ Chinese Acad Sci, Inst Automat,Natl Lab Pattern Recognit, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
2.Birkbeck Coll, Dept Comp Sci & Informat Syst, Malet St, London WC1E 7HX, England
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Hu, Weiming,Tian, Guodong,Kang, Yongxin,et al. Dual Sticky Hierarchical Dirichlet Process Hidden Markov Model and Its Application to Natural Language Description of Motions[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2018,40(10):2355-2373.
APA Hu, Weiming,Tian, Guodong,Kang, Yongxin,Yuan, Chunfeng,&Maybank, Stephen.(2018).Dual Sticky Hierarchical Dirichlet Process Hidden Markov Model and Its Application to Natural Language Description of Motions.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,40(10),2355-2373.
MLA Hu, Weiming,et al."Dual Sticky Hierarchical Dirichlet Process Hidden Markov Model and Its Application to Natural Language Description of Motions".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 40.10(2018):2355-2373.
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