• DocumentCode
    523543
  • Title

    Detection of Obstacle Based on Nocular Vision

  • Author

    Wang, Xiao-hua ; Fu, Wei-ping ; Chen, Wei

  • Author_Institution
    Coll. of Electron. & Inf., Xi´´an Polytech. Univ., Xi´´an, China
  • Volume
    2
  • fYear
    2010
  • fDate
    11-12 May 2010
  • Firstpage
    71
  • Lastpage
    74
  • Abstract
    A leading obstacles detection and recognition method based on binocular vision is mainly described in this paper. An edge fitting method based on the regional border of fusion segmentation image is used in fitting the edge of road, a viable region and suspected obstacles are obtained through fusing the fusion segmentation image and fitted edge. The method of partial edge contour extraction is used to detection the obstacles; the suspected obstacle edge contour is mapped to corresponding image through the homography, and then the parallax calculation of the two corresponding edges is used to distinguish obstacle and false obstacles accurately. In the outdoor environment, real-time obstacle detection can be realized. Experimental results in real outdoor scene show that the method can effectively detect obstacles.
  • Keywords
    collision avoidance; edge detection; image fusion; image recognition; image segmentation; robot vision; traffic engineering computing; edge contour extraction; edge fitting method; image segmentation fusion; nocular vision; obstacle detection; outdoor environment; parallax calculation; recognition method; Calibration; Cameras; Computer vision; Data mining; Educational institutions; Image edge detection; Image segmentation; Layout; Machine vision; Roads; inverse projection transformation; obstacle detection; partial contour extraction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4244-7279-6
  • Electronic_ISBN
    978-1-4244-7280-2
  • Type

    conf

  • DOI
    10.1109/ICICTA.2010.345
  • Filename
    5522576