Robust vanishing point estimation has been widely applied to various applications in the field of computer vision and pattern recognition for robotics, advanced driver assistance systems, and autonomous driving vehicles. The major challenge for vanishing point detection lies in line segments, spurious vanishing candidate removal, and clustering for refinement. Recent vanishing point detection approaches have attempted to reduce the computational complexity involved with voting processes using optimized voter selection strategies to identify the vanishing point from line segments. This paper proposes a novel vanishing point detection method to select robust candidates, applying optimized minimum spanning tree-based clustering of the vanishing point candidates by analyzing the lines within a unit sphere domain. The proposed scheme was applied to an open database that included illumination, partial occlusion, and viewpoint changes to validate robustness without prior scene information.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Optimized Clustering Scheme-Based Robust Vanishing Point Detection


    Contributors:


    Publication date :

    2020-01-01


    Size :

    7647496 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Line Clustering with Vanishing Point and Vanishing Line

    Minagawa, A. / Tagawa, N. / Moriya, T. et al. | British Library Conference Proceedings | 1999


    Line clustering with vanishing point and vanishing line

    Minagawa, A. / Tagawa, N. / Moriya, T. et al. | IEEE | 1999


    Vanishing point detection

    Tai, A. / Kittler, J. / Petrou, M. et al. | British Library Online Contents | 1993



    Fast and Robust Vanishing Point Detection for Unstructured Road Following

    Shi, Jinjin / Wang, Jinxiang / Fu, Fangfa | IEEE | 2016