The multi-object Bayes (MOB) filter uses random finite sets (RFSs) to represent a scene. A drawback of this filter is the computational complexity of the multi-object likelihood function. In this contribution, an approximation of the multi-object likelihood function is presented allowing for real-time implementation on a graphics processing unit using sequential Monte Carlo (SMC) methods. Additionally, a track extraction algorithm using clustering as well as an approach to determine the existence probability of each single object are proposed.


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    Titel :

    Real-Time Multi-Object Tracking using Random Finite Sets


    Beteiligte:
    Reuter, Stephan (Autor:in) / Wilking, Benjamin (Autor:in) / Wiest, Jurgen (Autor:in) / Munz, Michael (Autor:in) / Dietmayer, Klaus (Autor:in)


    Erscheinungsdatum :

    2013-10-01


    Format / Umfang :

    2376463 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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