• DocumentCode
    1613548
  • Title

    Road vanishing-point detection: A multi-population genetic algorithm based approach

  • Author

    Keyu Lu ; Jian Li ; Xiangjing An ; Hangen He

  • Author_Institution
    Coll. of Mechatron. & Autom., Nat. Univ. of Defense Technol., Changsha, China
  • fYear
    2013
  • Firstpage
    415
  • Lastpage
    419
  • Abstract
    In this paper, we propose a robust method for the fast estimation of the road vanishing point in challenging scenarios based on multiple population genetic algorithm (MPGA). The proposed method consists of three main parts: the first part is a searching method which searches for vanishing point candidates by multiple population genetic algorithm, the second part is a locally tangent-based voting method which obtains the value of the fitness function for the first part, and the last part is a local dominant texture orientation estimation method which applies Gabor filter banks to estimate the local dominant orientation of the vanishing point candidate and its voters for the second part. The proposed method has been tested on a vanishing point dataset which contains over 200 various road images. The experimental results demonstrate that the proposed method is both efficient and effective in detecting vanishing point compared with some state-of-the-art methods.
  • Keywords
    Gabor filters; channel bank filters; genetic algorithms; image texture; object detection; Gabor filter banks; MPGA; fitness function; local dominant texture orientation estimation method; locally tangent-based voting method; multipopulation genetic algorithm; road images; road vanishing-point detection; searching method; vanishing point candidates; Accuracy; Biological cells; Estimation; Genetic algorithms; Roads; Sociology; Statistics; Gabor filter bank; MPGA; vanishing point detection; voting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Chinese Automation Congress (CAC), 2013
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4799-0332-0
  • Type

    conf

  • DOI
    10.1109/CAC.2013.6775770
  • Filename
    6775770