Segmentation of the scene is a fundamental component in computer vision to find regions of interest. Most systems that aspire to run in real-time use a fast segmentation stage that considers the whole image, and then a more costly stage for classification. In this paper we present a novel approach to segment moving objects from images taken with a moving camera. The segmentation algorithm is based on a special representation of optical flow, on which u-disparity is applied. The u-disparity is used to indirectly find and mask out the background flow in the image, by approximating it with a quadratic function. Robustness in the optical flow calculation is achieved by contrast content filtering. The algorithm successfully segments moving pedestrians from a moving vehicle with few false positive segments. Most false positive segments are due to poles and organic structures, such as trees. Such false positives are, however, easily rejected in a classification stage. The presented segmentation algorithm is intended to be used as a component in a detection/classification framework.


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    Title :

    Fast object segmentation from a moving camera


    Contributors:
    Arnell, F. (author) / Petersson, L. (author)


    Publication date :

    2005-01-01


    Size :

    1093867 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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