This paper addresses the problem of tracking human body pose in monocular video including automatic pose initialization and re-initialization after tracking failures caused by partial occlusion or unreliable observations. We proposed a method based on data-driven Markov chain Monte Carlo (DD-MCMC) that uses bottom-up techniques to generate state proposals for pose estimation and initialization. This method allows us to exploit different image cues and consolidate the inferences using a representation known as the proposal maps. We present experimental results with an indoor video sequence.


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    Titel :

    Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach


    Beteiligte:
    Lee, Mun Wai (Autor:in) / Nevatia, Ramakant (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    380216 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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