This paper proposes a framework of segment-based free space estimation using plane normal vector with stereo vision. An image is divided into compact superpixels and each of them is viewed as a plane composed of the normal vector in disparity space. To deal with the variation of illumination and shading in real traffic scenes, we estimate depth information for the segmented stereo pair. The representative normal vector is then computed at superpixel-level, which alleviates the problems of conventional color-based approaches and depth-based approaches simultaneously. Based on the assumption that the central-bottom of input image is navigable region, the free space is then determined by clustering the plane normal vectors with the K-means algorithm. In experiments, the proposed approach is evaluated on the KITTI dataset in which we provide the ground truth labels for free space region. The experimental results demonstrate that the proposed framework effectively estimates the free space under various real traffic scenes, and outperforms current state of the art methods both qualitatively and quantitatively.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Segment-based free space estimation using plane normal vector in disparity space


    Contributors:


    Publication date :

    2015-10-01


    Size :

    1893313 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Plane detection in disparity space

    Trucco, E. / Isgro, F. / Bracchi, F. et al. | British Library Conference Proceedings | 2003


    Color-Based Free-Space Segmentation Using Online Disparity-Supervised Learning

    Sanberg, Willem P. / Dubbelman, Gijs / With, Peter H. N. de | IEEE | 2015



    Closed-form Linear Solution To Motion Estimation In Disparity Space

    Derpanis, K. G. / Chang, P. | British Library Conference Proceedings | 2006