In this paper, we propose a robust vanishing-point constrained lane detection method with a stereo-rig. This method can achieve promising detection performance for both straight and curved lanes without assuming any parametric lane model. First, we propose an accurate and efficient road vanishing point detection scheme based on the v-disparity and visual odometry techniques, where the v-disparity map can significantly reduce the searching space for vanishing point, and the visual odometry can benefit the vanishing point detection of both straight and curved roads. Next, we formulate the lane detection problem as a graph-search procedure, where a vanishing-point constrained Dijkstra shortest-path lane model is proposed to obtain a minimum-cost map. The two lane borders can be detected by finding two optimal paths which originate from the vanishing point to two cost-map derived terminal points, respectively. The proposed method has been tested on the KITTI and the Oxford RobotCar data sets and it works accurately and robustly on a variety of road scenes.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vanishing Point Constrained Lane Detection With a Stereo Camera


    Contributors:
    Su, Yingna (author) / Zhang, Yigong (author) / Lu, Tao (author) / Yang, Jian (author) / Kong, Hui (author)


    Publication date :

    2018-08-01


    Size :

    2038877 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    LANE DETECTION METHOD USING DISPARITY BASED ON VANISHING POINT

    HAN DONG SEOG / SEO CHAN HEE / LEE MIN HO et al. | European Patent Office | 2016

    Free access


    Multiple Lane Detection Algorithm Based on Novel Dense Vanishing Point Estimation

    Ozgunalp, Umar / Fan, Rui / Ai, Xiao et al. | IEEE | 2017



    Vanishing Point Based Lane Departure Warning Using Template-Based Detection and Tracking of Lane Markers

    Kumar, Ammu M. / Simon, Philomina / Kavitha, R. | Springer Verlag | 2017