• DocumentCode
    1534592
  • Title

    Road traffic sign detection and classification

  • Author

    de la Escalera, A. ; Moreno, Luis E. ; Salichs, Miguel Angel ; Armingol, Jose Maria

  • Author_Institution
    Area de Ingenieria de Sistemas y Autom., Univ. Carlos III de Madrid, Spain
  • Volume
    44
  • Issue
    6
  • fYear
    1997
  • fDate
    12/1/1997 12:00:00 AM
  • Firstpage
    848
  • Lastpage
    859
  • Abstract
    A vision-based vehicle guidance system for road vehicles can have three main roles: (1) road detection; (2) obstacle detection; and (3) sign recognition. The first two have been studied for many years and with many good results, but traffic sign recognition is a less-studied field. Traffic signs provide drivers with very valuable information about the road, in order to make driving safer and easier. The authors think that traffic signs most play the same role for autonomous vehicles. They are designed to be easily recognized by human drivers mainly because their color and shapes are very different from natural environments. The algorithm described in this paper takes advantage of these features. It has two main parts. The first one, for the detection, uses color thresholding to segment the image and shape analysis to detect the signs. The second one, for the classification, uses a neural network. Some results from natural scenes are shown
  • Keywords
    computer vision; driver information systems; image classification; image segmentation; neural nets; road vehicles; color thresholding; image classification; image segmentation; image shape analysis; neural network; road traffic sign classification; road traffic sign detection; road vehicles; vision-based vehicle guidance system; Humans; Image analysis; Image color analysis; Image segmentation; Mobile robots; Navigation; Remotely operated vehicles; Road vehicles; Shape; Vehicle detection;
  • fLanguage
    English
  • Journal_Title
    Industrial Electronics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0278-0046
  • Type

    jour

  • DOI
    10.1109/41.649946
  • Filename
    649946