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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
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2018-10-01
Volume40Issue:10Pages:2355-2373
SubtypeArticle
AbstractIn 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.
KeywordHdp-hmm Sticky Prior Motion Pattern Learning Natural Language Description
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TPAMI.2017.2756039
WOS KeywordTRAJECTORY ANALYSIS ; SAMPLING METHODS ; VIDEO RETRIEVAL ; RECOGNITION ; PATTERNS ; SYSTEM ; PRIORS
Indexed BySCI
Language英语
Funding Organization973 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 Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000443875500006
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21828
Collection模式识别国家重点实验室_视频内容安全
Affiliation1.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
Recommended Citation
GB/T 7714
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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