The paper investigates the unsupervised learning of a model of activity for a multi-camera surveillance network that can be created from a large set of observations. This enables the learning algorithm to establish links between camera views associated with an activity. The learning algorithm operates in a correspondence-free manner, exploiting the statistical consistency of the observation data. The derived model is used to automatically determine the topography of a network of cameras and to provide a means for tracking targets across the "blind" areas of the network. A theoretical justification and experimental validation of the methods are provided.


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    Title :

    Bridging the gaps between cameras


    Contributors:
    Makris, D. (author) / Ellis, T. (author) / Black, J. (author)


    Publication date :

    2004-01-01


    Size :

    363497 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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