This article deals with the following issue: how to track a varying number of pedestrians through observations by means of a 4-plane laser sensor. In order to answer to the multiple target tracking problem and more specifically pedestrian tracking, we propose in this paper a statistical approach using a particle filter based on nonparametric data association methods. This approach allows to go beyond the conventional Gaussian assumption and to use as well as possible each particle during track/observation association by means of either a “Parzen Window” kernel method or a K-nearest neighbor algorithm. Simulated and experimental results show the relevance of this method compared to the usual Gaussian window methods.


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    Title :

    Nonparametric data association for particle filter based multi-object tracking: application to multi-pedestrian tracking


    Contributors:


    Publication date :

    2008-06-01


    Size :

    580088 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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