We address the problem of tracking multiple objects encountered in many situations in signal or image processing. We consider stochastic dynamic systems nonlinearly and incompletely observed. The difficulty lies on the fact that the estimation of the states requires the assignation of the observations to the multiple targets. We propose an extension of the classical particle filter where the stochastic vector of assignation is estimated by a Gibbs sampler. The merit of the method is assessed in bearings-only context and we present one application in image-based tracking.


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    Title :

    A particle filter to track multiple objects


    Contributors:
    Hue, C. (author) / Le Cadre, J.-P. (author) / Perez, P. (author)


    Publication date :

    2001-01-01


    Size :

    789239 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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