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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach


    Contributors:


    Publication date :

    2005-01-01


    Size :

    380216 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Markov Chain Monte Carlo Modular Ensemble Tracking

    Penne, T. / Tilmant, C. / Chateau, T. et al. | British Library Online Contents | 2013


    Sensitivity Analysis of Markov Chain Monte Carlo

    Millwater, Harry / Vazquez, Eric / Wu, Justin et al. | AIAA | 2010


    Efficient Random Vibration Analysis Using Markov Chain Monte Carlo Simulation

    Nikolaidis, Efstratios / Norouzi, Mahdi | SAE Technical Papers | 2012


    Efficient random vibration analysis using Markov chain Monte Carlo simulation

    Norouzi,M. / Nikolaidis,E. / Univ.of Toledo,US | Automotive engineering | 2012