In this paper, we address the problem of detection and tracking of group and individual targets. In particular, we focus on a group model with a virtual leader which models the bulk or group parameter. To perform the sequential inference, we propose a Markov Chain Monte Carlo (MCMC)-based Particle algorithm with a marginalisation scheme using pairwise Kalman filters. Numerical simulations illustrate the ability of the algorithm to detect and track targets within groups, as well as infer both the correct group structure and the number of targets over time.


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    Title :

    Tracking of coordinated groups using marginalised MCMC-based Particle algorithm


    Contributors:


    Publication date :

    2009-03-01


    Size :

    252225 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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