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    Dual sticky hierarchical Dirichlet process hidden Markov model and its application to natural language description of motions

    Hu, W. and Tian, G. and Kang, Y. and Yuan, C. and Maybank, Stephen (2017) 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 (99), ISSN 0162-8828.

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    In this paper, a new nonparametric Bayesian model called the dual sticky hierarchical Dirichlet process hidden Markov modle (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, semantic motion regions, and the learnt sequences of atomic activities. the action represented by the trajectory can be described in natural language in as autometic 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.


    Item Type: Article
    Additional Information: (c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    Keyword(s) / Subject(s): HDP-HMM, Sticky prior, Motion pattern learning, Natural language description
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Stephen Maybank
    Date Deposited: 27 Sep 2017 13:03
    Last Modified: 03 Jul 2020 15:54


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