Autonomous collision avoidance in vehicles requires an accurate separation of obstacles from the background, particularly near the focus of expansion. This paper presents 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 with a monocular camera setup has not been feasible up to now. This 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.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Quadtree Segmentation Network for Obstacle Avoidance in Monocular Navigation

    Braun, Daniel / Morel, Olivier / Vasseur, Pascal et al. | IEEE | 2022


    Realtime depth estimation and obstacle detection from monocular video

    Wedel, Andreas / Franke, Uwe / Klappstein, Jens et al. | Tema Archiv | 2006


    FastRoadSeg: Fast Monocular Road Segmentation Network

    Gong, Shi / Zhou, Huan / Xue, Feng et al. | IEEE | 2022


    Obstacle Avoidance Using a Monocular Camera

    Hatch, Kyle / Mern, John M. / Kochenderfer, Mykel J. | AIAA | 2021


    OBSTACLE AVOIDANCE USING A MONOCULAR CAMERA

    Hatch, Kyle / Mern, John M. / Kochenderfer, Mykel J. | TIBKAT | 2021