We describe a probabilistic framework for detecting and tracking motion boundaries. It builds on previous work (M.J. Black and D.J. Fleet, 2000) that used a particle filter to compute a posterior distribution over multiple, local motion models, one of which was specific for motion boundaries. We extend that framework in two ways: 1) with an enhanced likelihood that combines motion and edge support, 2) with a spatiotemporal model that propagates beliefs between adjoining image neighborhoods to encourage boundary continuity and provide better temporal predictions for motion boundaries. Approximate inference is achieved with a combination of tools: sampled representations allow us to represent multimodal non-Gaussian distributions and to apply nonlinear dynamics, while mixture models are used to simplify the computation of joint prediction distributions.


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    Titel :

    Probabilistic tracking of motion boundaries with spatiotemporal predictions


    Beteiligte:
    Nestares, O. (Autor:in) / Fleet, D.J. (Autor:in)


    Erscheinungsdatum :

    2001-01-01


    Format / Umfang :

    1773055 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Probabilistic Tracking of Motion Boundaries with Spatiotemporal Predictions

    Nestares, O. / Fleet, D. J. / IEEE | British Library Conference Proceedings | 2001


    Probabilistic Detection and Tracking of Motion Boundaries

    Black, M. J. / Fleet, D. J. | British Library Online Contents | 2000



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