Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow based tracker. Instead of minimizing an intensity difference function between successive frames, SVT maximizes the SVM classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a coarse-to-fine approach in the classification stage. We show results of using a homogeneous quadratic polynomial kernel-SVT for vehicle tracking in image sequences.


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    Title :

    Support Vector Tracking


    Contributors:
    Avidan, S. (author)


    Publication date :

    2001-01-01


    Size :

    1216803 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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