Speed Limit Sign Recognition Using Log-Polar Mapping and Visual Codebook

  • Bing Liu
  • Huaping Liu
  • Xiong Luo
  • Fuchun Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7368)


Traffic sign recognition is one of the hot issues on the modern driving assistance. In recent years, the method using Bag-of-Word (BOW) model for image recognition has gained its popularity upon its simplicity and efficiency. The conventional approach based on BOW requires nonlinear classifiers to get a good image recognition accuracy. Instead, a method called Locality-constrained Linear Coding(LLC) presents an effective strategy for coding, and only with a simple linear classifier could achieve a good effect. LLC uses uniform sampling for feature extraction, but allowing for features of traffic signs, the central vision information of the image is more important than the surroundings. Fortunately, log-polar mapping to preprocess image samples before coding is helpful for traffic sign recognition. In this paper, a combination method of log-polar mapping and LLC algorithm is presented to achieve a high image classification performance up to 97.3141% on speed limit sign in the GTSRB dataset.


Speed limit sign recognition sparse coding log-polar mapping GTSRB dataset 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bing Liu
    • 1
  • Huaping Liu
    • 2
    • 3
  • Xiong Luo
    • 1
  • Fuchun Sun
    • 2
    • 3
  1. 1.School of Computer and Communication EngineeringUniversity of Science and TechnologyBeijingP.R. China
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityP.R. China
  3. 3.State Key Laboratory of Intelligent Technology and SystemsBeijingP.R. China

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