Modern driver assistance systems such as collision avoidance or intersection assistance need reliable information on the current environment. Extracting such information from camera-based systems is a complex and challenging task for inner city traffic scenarios. This paper presents an approach for object detection utilizing sparse scene flow. For consecutive stereo images taken from a moving vehicle, corresponding interest points are extracted. Thus, for every interest point, disparity and optical flow values are known and consequently, scene flow can be calculated. Adjacent interest points describing a similar scene flow are considered to belong to one rigid object. The proposed method does not rely on object classes and allows for a robust detection of dynamic objects in traffic scenes. Leading vehicles are continuously detected for several frames. Oncoming objects are detected within five frames after their appearance.


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    Title :

    Sparse scene flow segmentation for moving object detection in urban environments


    Contributors:
    Lenz, P. (author) / Ziegler, J. (author) / Geiger, A. (author) / Roser, M. (author)


    Publication date :

    2011-06-01


    Size :

    1843806 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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