Objects can exhibit different dynamics at different scales, and this is often exploited by visual tracking algorithms. A local dynamic model is typically used to extract image features that are then used as input to a system for tracking the entire object using a global dynamic model. Approximate local dynamics may be brittle - point trackers drift due to image noise and adaptive background models adapt to foreground objects that become stationary - but constraints from the global model can make them more robust. We propose a probabilistic framework for incorporating global dynamics knowledge into the local feature extraction processes. A global tracking algorithm can be formulated as a generative model and used to predict feature values that are incorporated into an observation process of the feature extractor. We combine such models in a multichain graphical model framework. We show the utility of our framework for improving feature tracking and thus shape and motion estimates in a batch factorization algorithm. We also propose an approximate filtering algorithm appropriate for online applications, and demonstrate its application to background subtraction.


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    Title :

    Combining object and feature dynamics in probabilistic tracking


    Contributors:
    Taycher, L. (author) / Fisher, J.W. (author) / Darrell, T. (author)


    Publication date :

    2005-01-01


    Size :

    876969 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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