A contour based stereo vision algorithm for video surveillance applications

In this paper we describe a real-time stereo vision algorithm that determines the disparity map of a given scene by an evaluation of the object contours, relying on a reference image displaying the scene without objects. Contours are extracted from the full-resolution absolute difference image between current and reference image, essentially by binarization with several locally adaptive thresholds. To estimate disparity values, contour segments extending over several epipolar lines are used. This approach leads to very accurate disparity values. The algorithm can be configured such that no image region that differs from the reference image by more than a given minimum statistical significance is overlooked, which makes it especially suitable for safety applications. We successfully apply this contour based stereo vision (CBS) algorithm to the task of video surveillance of hazardous areas in the production environment. Under the harsh conditions encountered, the CBS algorithm achieves to faithfully detect objects entering the scene and determine their three-dimensional structure. What is more, it turns out to cope with small objects and very difficult illumination and contrast settings.