• DocumentCode
    1503326
  • Title

    A New Approach to Urban Pedestrian Detection for Automatic Braking

  • Author

    Broggi, Alberto ; Cerri, Pietro ; Ghidoni, Stefano ; Grisleri, Paolo ; Jung, Ho Gi

  • Author_Institution
    Dipt. di Ing. dell´´Inf., Univ. di Parma, Parma, Italy
  • Volume
    10
  • Issue
    4
  • fYear
    2009
  • Firstpage
    594
  • Lastpage
    605
  • Abstract
    This paper presents an application of a pedestrian-detection system aimed at localizing potentially dangerous situations under specific urban scenarios. The approach used in this paper differs from those implemented in traditional pedestrian-detection systems, which are designed to localize all pedestrians in the area in front of the vehicle. Conversely, this approach searches for pedestrians in critical areas only. The environment is reconstructed with a standard laser scanner, whereas the following check for the presence of pedestrians is performed due to the fusion with a vision system. The great advantages of such an approach are that pedestrian recognition is performed on limited image areas, therefore boosting its timewise performance, and no assessment on the danger level is finally required before providing the result to either the driver or an onboard computer for automatic maneuvers. A further advantage is the drastic reduction of false alarms, making this system robust enough to control nonreversible safety systems.
  • Keywords
    braking; computer vision; image recognition; object detection; traffic engineering computing; automatic braking; automatic maneuvers; laser scanner; nonreversible safety systems; pedestrian recognition; pedestrian-detection system; urban pedestrian detection; vision system; Artificial intelligence (AI); computer vision; fuzzy logic; image processing; pattern recognition; pedestrian detection;
  • fLanguage
    English
  • Journal_Title
    Intelligent Transportation Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1524-9050
  • Type

    jour

  • DOI
    10.1109/TITS.2009.2032770
  • Filename
    5290131