Discriminant Splitting of Regions in Traffic Sign Recognition

  • Sergio Lafuente-Arroyo
  • Roberto J. López-Sastre
  • Saturnino Maldonado-Bascón
  • Rafael Martínez-Tomás
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7931)


Mining discriminative spatial patterns in image data is a subject of interest in traffic sign recognition. In this paper, we use an approach for detecting spatial regions that are highly discriminative. The main idea is to search the normalized size blobs for discriminative regions by adaptively partitioning the space into progressively smaller sub-regions. Thus, each cluster of signs is characterized by an unique region pattern which consists of homogeneous and discriminant 2-D regions. The mean intensities of these regions are used as features. To evaluate the discriminative power of the attributes corresponding to detected regions, we performed classification experiments using a classifier based on Support Vector Machines. The proposed method has been tested in a real traffic sign database. Results demonstrate that the method can achieve a considerable reduction of features with respect to extraction from raw images while maintaining accurate.


feature extraction adaptive partitioning traffic sign classification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    de la Escalera, A., Armigol, J.M., Pastor, J.M., Rodriguez, F.J.: Visual sign information extraction and identification by deformable models for intelligent vehicles. IEEE Transactions on Intelligent Transportation Systems 5(2), 57–68 (2004)CrossRefGoogle Scholar
  2. 2.
    Hsien, J.C., Liou, Y.S., Chen, S.Y.: Road sign detection and recognition using hidden Markov model. Asian Journal of Health and Information Sciences 1, 85–100 (2006)Google Scholar
  3. 3.
    Larsson, F., Felsberg, M.: Using fourier descriptors and spatial models for traffic sign recognition. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 238–249. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Liu, Y., Ikenaga, T., Goto, S.: Geometrical, physical and text/symbol analysis based approach of traffic sign detection system. In: Proceedings of the IEEE Intelligent Vehicle Symposium, pp. 238–243 (2006)Google Scholar
  5. 5.
    Maldonado-Bascon, S., Lafuente-Arroyo, S., Gil-Jiménez, P., Gómez-Moreno, H., López-Ferreras, F.: Road-Sign Detection and Recognition Based on Support Vector Machines. IEEE Trans. on Intelligent Transportation Systems 8(2), 264–278 (2007)CrossRefGoogle Scholar
  6. 6.
    Maldonado-Bascon, S., Acevedo-Rodríguez, J., Lafuente-Arroyo, S., Fernández-Caballero, A., López-Ferreras, F.: An optimization on pictogram identification for the road-sign recognition task using SVMs. Computer Vision and Image Understanding 114(3), 373–383 (2010)CrossRefGoogle Scholar
  7. 7.
    Megalooikonomou, V., Kontos, D., Pokrajac, D., Lazarevic, A., Obradovic, Z.: An adaptive partitioning approach for mining discriminant regions in 3D image data. Journal of Intelligent Information Systems Archive 31(3), 217–242 (2008)CrossRefGoogle Scholar
  8. 8.
    Nguwi, Y.Y., Cho, S.Y.: Two-tier self-organizing visual model for road sign recognition. In: Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pp. 794–799 (2008)Google Scholar
  9. 9.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9, 62–66 (1979)CrossRefGoogle Scholar
  10. 10.
    Shaposhnikov, W.D., Shaposhnikov, D.G., Lubov, N., Golovan, E.V., Shevtsova, A.: Road sign recognition by single positioning of space-variant sensor window. In: Proceedings of the 15th International Conference on Vision Interface (2002)Google Scholar
  11. 11.
    Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The german traffic sign recognition benchmark: a multi-class classification competition. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 1453–1460 (2011)Google Scholar
  12. 12.
    Timofte, R., Zimmermann, K., Van Gool, L.: Multi-view traffic sign detection, recognition and 3D localisation. Journal of Machine Vision and Applications, 1–15 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sergio Lafuente-Arroyo
    • 1
  • Roberto J. López-Sastre
    • 1
  • Saturnino Maldonado-Bascón
    • 1
  • Rafael Martínez-Tomás
    • 2
  1. 1.GRAM, Department of Signal Theory and CommunicationsUAHAlcalá de HenaresSpain
  2. 2.Department of Artificial IntelligenceUNEDMadridSpain

Personalised recommendations