• DocumentCode
    2950681
  • Title

    Fast and Robust Traffic Sign Detection

  • Author

    Soetedjo, Aryuanto ; Yamada, Koichi

  • Author_Institution
    Inf. Sci. & Control Eng., Nagaoka Univ. of Technol.
  • Volume
    2
  • fYear
    2005
  • fDate
    12-12 Oct. 2005
  • Firstpage
    1341
  • Lastpage
    1346
  • Abstract
    This paper deals with the fast and robust detection of the traffic sign images. A new technique called geometric fragmentation is proposed to detect the red circular traffic signs. It detects the outer ellipses of the signs by combining the left and right fragments of the ellipse objects. A search based on the geometric fragmentation is used to find the ellipse fragments. This search is somewhat similar to genetic algorithm (GA) in the sense that it employs the terms of individual, population, crossover, and objective function usually used in GA. To increase the accuracy and reduce the computational time, a new objective function is introduced for evaluating the individuals. The algorithm was tested for detecting the red circular traffic signs from the real scene image. The experimental results show that the proposed algorithm has a higher detection rate with a lower computational cost compared with the referential genetic algorithm-based ellipse detection
  • Keywords
    computational geometry; feature extraction; genetic algorithms; object detection; traffic engineering computing; ellipse detection; genetic algorithm; geometric fragmentation; objective function; red circular traffic signs; traffic sign image detection; Computational efficiency; Genetic algorithms; Layout; Object detection; Robustness; Testing; Traffic sign detection; ellipse detection; genetic algorithm; geometric fragmentation; objective function;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 2005 IEEE International Conference on
  • Conference_Location
    Waikoloa, HI
  • Print_ISBN
    0-7803-9298-1
  • Type

    conf

  • DOI
    10.1109/ICSMC.2005.1571333
  • Filename
    1571333