The paper presents a new approach for continuous tracking of moving objects observed by multiple fixed cameras. The continuous tracking of moving objects in each view is realized using a tensor voting based approach. We infer objects' trajectories by performing a perceptual grouping in 2D+t using tensor voting. Also, a multi-scale approach to bridge gaps in object trajectories is presented The trajectories obtained from the multiple cameras are registered in space and time, allowing a synchronization of the video streams and a continuous tracking of objects across multiple views. We demonstrate the performance of the system on several real video surveillance sequences.


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    Titel :

    Continuous multi-views tracking using tensor voting


    Beteiligte:
    Jinman Kang, (Autor:in) / Cohen, I. (Autor:in) / Medioni, G. (Autor:in)


    Erscheinungsdatum :

    01.01.2002


    Format / Umfang :

    1053414 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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