We describe the theory of a detect-track-confirm filter whose role is moving target detection and clutter suppression in surveillance data. The filter has broad generality due to the minimal assumptions made in developing the theory. Track confirmation is decided on the basis of a probability measure that is fully computable from clutter properties measured from surveillance data, without needing to assume target properties such as trajectory or detectability. Experimental results on real surveillance datasets are presented.


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    Title :

    Detect-track-confirm filter with minimal constraints


    Contributors:
    Caprari, R.S. (author) / Goh, A.S. (author)


    Publication date :

    2004-01-01


    Size :

    510838 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English