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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    Title :

    Real-Time Multi-Object Tracking using Random Finite Sets


    Contributors:


    Publication date :

    2013-10-01


    Size :

    2376463 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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