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A Fast Method to Detect and Recognize Scaled and Skewed Road Signs

  • Yi-Sheng Liou
  • Der-Jyh Duh
  • Shu-Yuan Chen
  • Jun-Wei Hsieh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3708)

Abstract

A fast method to detect and recognize scaled and skewed road signs is proposed in this paper. The input color image is first quantized in HSV color model. Border tracing those regions with the same colors as road signs is adopted to find the regions of interest (ROI). Verification is then performed to find those ROIs satisfying specific constraints as road sign candidates. The candidate regions are extracted and normalization is automatically calculated to handle scaled and skewed road signs. Finally, matching based on distance maps is adopted to measure the similarity between the scene and model road signs to accomplish recognition. Experimental results show that the proposed method is effective and efficient, even for scaled and skewed road signs in complicated scenes. On the average, it takes 4–50 and 11 ms for detection and recognition, respectively. Thus, the proposed method is adapted to be implemented in real time.

Keywords

Binary Image Recognition Accuracy Scene Image Road Sign Color Quantization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yi-Sheng Liou
    • 1
  • Der-Jyh Duh
    • 1
  • Shu-Yuan Chen
    • 1
  • Jun-Wei Hsieh
    • 2
  1. 1.Department of Computer Science and Engineering 
  2. 2.Department of Electrical Engineering Yuan Ze UniversityTaoYuanTaiwan, R.O.C

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