Particle filters are used for hidden state estimation with nonlinear dynamical systems. The inference of 3-D human motion is a natural application, given the nonlinear dynamics of the body and the nonlinear relation between states and image observations. However, the application of particle filters has been limited to cases where the number of state variables is relatively small, because the number of samples needed with high dimensional problems can be prohibitive. We describe a filter that uses hybrid Monte Carlo (HMC) to obtain samples in high dimensional spaces. It uses multiple Markov chains that use posterior gradients to rapidly explore the state space, yielding fair samples from the posterior. We find that the HMC filter is several thousand times faster than a conventional particle filter on a 28 D people tracking problem.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    People tracking using hybrid Monte Carlo filtering


    Beteiligte:
    Kiam Choo, (Autor:in) / Fleet, D.J. (Autor:in)


    Erscheinungsdatum :

    01.01.2001


    Format / Umfang :

    807368 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    People Tracking Using Hybrid Monte Carlo Filtering

    Choo, K. / Fleet, D. / IEEE | British Library Conference Proceedings | 2001


    Hybrid Monte Carlo Filtering: Edge-Based People Tracking

    Poon, E. / Fleet, D. J. | British Library Conference Proceedings | 2002