A novel method for the simultaneous modeling and tracking (SMAT) of a feature set during motion sequence is proposed. The method requires no prior information. Instead the a posteriori distribution of appearance and shape is built up incrementally using an exemplar based approach. The resulting model is less optimal than when a priori data is used, but can be built in real-time. Data in any form may be used, provided a distance measure and a means to classify outliers exists. Here, a two tier implementation of SMAT is used: at the feature level, mutual information is used to track image patches; and at the object level, a structure model is built from the feature positions. As experiments demonstrate, the tracker is robust and operates in real-time without requiring prelearned data.


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    Title :

    Simultaneous modeling and tracking (SMAT) of feature sets


    Contributors:
    Dowson, N.D.H. (author) / Bowden, R. (author)


    Publication date :

    2005-01-01


    Size :

    422075 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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