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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)

Abstract

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.

Keywords

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

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References

  1. 1.
    Liu, W., Lv, J., Gao, H., Duan, B., Yuan, H., Zhao, H.: An efficient real-time speed limit signs recognition based on rotation invariant feature. In: Proc. of Intelligent Vehicles Symposium (IV), pp. 1000–1005 (2011)Google Scholar
  2. 2.
    Fei-Fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: Proc. of Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 524–531 (2005)Google Scholar
  3. 3.
    Kardkovacs, Z., Paroczi, Z., Varga, E., Siegler, A., Lucz, P.: Real-time traffic sign recognition system. In: Proc. of Second International Conference on Cognitive Infocommunications (CogInfoCom), pp. 1–5 (2011)Google Scholar
  4. 4.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proc. of Computer Vision and Pattern Recognition (CVPR), pp. 2169–2178 (2006)Google Scholar
  5. 5.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. of Comp. Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  6. 6.
    Maldonado-Bascon, S., Lafuente-Arroyo, S., Gil-Jimenez, P., Gomez-Moreno, H., Lopez-Ferreras, F.: Road-sign detection and recognition based on support vector machines. IEEE Trans. on Intelligent Transportation Systems 8, 264–278 (2007)CrossRefGoogle Scholar
  7. 7.
    Paclik, P.: Road sign recognition survey, http://euler.fd.cvut.cz/research/rs2/files/skoda-rs-survey.html
  8. 8.
    Ruta, A., Li, Y., Liu, X.: Robust class similarity measure for traffic sign recognition. IEEE Trans. on Intelligent Transportation Systems 11, 846–855 (2010)CrossRefGoogle Scholar
  9. 9.
    Traver, V., Bernardino, A.: A review of log-polar imaging for visual perception in robotics. Robotics and Autonomous Systems 58, 378–398 (2010)CrossRefGoogle Scholar
  10. 10.
    Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: Proc. of Computer Vision and Pattern Recognition (CVPR), pp. 3360–3367 (2010)Google Scholar
  11. 11.
    Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: Proc. of Computer Vision and Pattern Recognition (CVPR), pp. 1794–1801 (2009)Google Scholar
  12. 12.
    Yu, K., Zhang, T., Gong, Y.: Nonlinear learning using local coordinate coding. In: Proc. of Advances in Neural Information Processing Systems (NIPS), pp. 1–9 (2009)Google Scholar

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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