WarpCut - Fast obstacle segmentation in monocular video

Abstract: Autonomous collision avoidance in vehicles requires an accurate seperation of obstacles from the background, particularly near the focus of expansion. In this paper, we present a technique for fast segmentation of stationary obstacles from video recorded by a single camera that is installed in a moving vehicle. The input image is divided into three motion segments consisting of the ground plane, the background, and the obstacle. This constrained scenario allows for good initial estimates of the motion models, which are iteratively refined during segmentation. The horizon is known due to the camera setup. The remaining binary partitioning problem is solved by a graph cut on the motion-compensated difference images. Obstacle segmentation in realistic scenes a monocular camera setup has not been feasible up to now. Our experimental evaluation shows that the proposed approach leads to fast and accurate obstacle segmentation and distance estimation without prior knowledge about the size, shape or base point of obstacles.