In this paper, we present a probabilistic tracking framework that combines sound and vision to achieve more robust and accurate tracking of multiple objects. In a cluttered or noisy scene, our measurements have a non-Gaussian, multi-modal distribution. We apply a particle filter to track multiple people using combined audio and video observations. We have applied our algorithm to the domain of tracking people with a stereo-based visual foreground detection algorithm and audio localization using a beamforming technique. Our model also accurately reflects the number of people present. We test the efficacy of our system on a sequence of multiple people moving and speaking in an indoor environment.


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    Title :

    A Probabilistic Framework for Multi-modal Multi-Person Tracking


    Contributors:


    Publication date :

    2003-06-01


    Size :

    681058 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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