This paper deals with the detection of arbitrary static objects in traffic scenes from monocular video using structure from motion. A camera in a moving vehicle observes the road course ahead. The camera translation in depth is known. Many structure from motion algorithms were proposed for detecting moving or nearby objects. However, detecting stationary distant obstacles in the focus of expansion remains quite challenging due to very small subpixel motion between frames In this work the scene depth is estimated from the scaling of supervised image regions. The authors generate obstacle hypotheses from these depth estimates in image space. A second step then performs testing of these by comparing with the counter hypothesis of a free driveway. The approach can detect obstacles already at distances of 50 m and more with a standard focal length. This early detection allows driver warning and safety precaution in good time.


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    Title :

    Realtime depth estimation and obstacle detection from monocular video


    Additional title:

    Tiefenschätzung und Hinderniserkennung in Echtzeit aus monokularen Videobildern


    Contributors:


    Publication date :

    2006


    Size :

    10 Seiten, 5 Bilder, 14 Quellen





    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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