We propose a computer vision algorithm that detects obstacles crossing a vehicle's path with a monocular camera mounted on the vehicle. False positives are strongly suppressed even for low-resolution images by imposing constraints on feature-based optical flows. The constraints are derived from a model of crossing obstacle motion under perspective projection. A key concept in this model is “Relative Incoming Angle”, which is an angle between the camera's translational direction and relative velocity of a crossing obstacle with respect to the camera. We show a ROC curve that has been obtained by varying the Relative Incoming Angle using our dataset consisting of 18 scenes, 1456 frames. A representative point on the curve yields the detection rate of 59.7% and false positive rate of 2.6% (per-image).


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    Title :

    Crossing obstacle detection with a vehicle-mounted camera


    Contributors:
    Sato, I. (author) / Yamano, C. (author) / Yanagawa, H. (author)


    Publication date :

    2011-06-01


    Size :

    2400696 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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