• DocumentCode
    2592597
  • Title

    Nighttime Vehicle Detection for Driver Assistance and Autonomous Vehicles

  • Author

    Chen, Yen-Lin ; Chen, Yuan-Hsin ; Chen, Chao-Jung ; Wu, Bing-Fei

  • Author_Institution
    Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu
  • Volume
    1
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    687
  • Lastpage
    690
  • Abstract
    This study presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting and locating vehicle headlights and taillights using techniques of image segmentation and pattern analysis. First, to effectively extract bright objects of interest, a segmentation process based on automatic multilevel thresholding is applied on the grabbed road-scene images. Then the extracted bright objects are processed by a rule-based procedure, to identify the vehicles by locating and analyzing their vehicle light patterns, and estimate their distances to the camera-assisted car. Experimental results demonstrate the effectiveness of the proposed method on detecting vehicles at night
  • Keywords
    driver information systems; feature extraction; image segmentation; object detection; automatic multilevel thresholding; autonomous vehicles; bright object extraction; camera-assisted car; driver assistance; image segmentation; nighttime vehicle detection; pattern analysis; road-scene images; rule-based identification; vehicle distance estimation; vehicle headlight location; vehicle light patterns; vehicle taillight location; Automotive components; Image segmentation; Image sequences; Layout; Mobile robots; Pattern analysis; Remotely operated vehicles; Road vehicles; Vehicle detection; Vehicle driving;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1051-4651
  • Print_ISBN
    0-7695-2521-0
  • Type

    conf

  • DOI
    10.1109/ICPR.2006.858
  • Filename
    1698985