A data fusion scheme for visual object identification and tracking by autonomous vehicles is recommended. Image motion vectors fields, color features, visual disparity depth information and camera motion parameters are fused together to identify the target 3D (Three-Dimensional) visual and dynamic features. A detailed description of the 3D target tracking algorithm using an extended Kalman Filter with a constant velocity dynamic model is presented. Performance of the proposed scheme is discussed through experimental results.


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    Title :

    Camera motion and visual information fusion for 3D target tracking


    Additional title:

    Fusion der Kamerabewegung und der visuellen Informationen für die dreidimensionale Zielverfolgung


    Contributors:


    Publication date :

    2004


    Size :

    6 Seiten, 10 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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