• DocumentCode
    456783
  • Title

    Multiple Probabilistic Templates Based Pedestrian Detection in Night Driving with a Normal Camera

  • Author

    Hu, Mei

  • Author_Institution
    Dept. of Autom., Tsinghua Univ., Beijing
  • Volume
    2
  • fYear
    2006
  • fDate
    Aug. 30 2006-Sept. 1 2006
  • Firstpage
    574
  • Lastpage
    577
  • Abstract
    Pedestrian detection is particularly challenging, comparing with other targets in the domain of object detection, especially for night driving just with a normal camera. In this paper we combine two probabilistic templates based classifiers for elaborate pedestrian detection: the binary probabilistic template based classifier (BPTC) as the first layer to reject most of non-pedestrians by the features of binary image; the gray probabilistic template based classifier (GPTC) as the second layer to make the final classification by the gray probability, which is the contribution of this paper. Experiments show that our approach performs well most of the time, and the system can achieve real-time detection
  • Keywords
    automated highways; cameras; feature extraction; image classification; image segmentation; object detection; probability; feature extraction; image classification; multiple probabilistic template based classifier; night driving; object detection; pedestrian detection; Adaptive filters; Automation; Cameras; Computer crashes; Image edge detection; Injuries; Intelligent transportation systems; Object detection; Road accidents; Shape;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    0-7695-2616-0
  • Type

    conf

  • DOI
    10.1109/ICICIC.2006.315
  • Filename
    1692052