Machine Vision and Applications

, Volume 21, Issue 2, pp 99–111 | Cite as

Traffic sign recognition system with β -correction

  • Sergio Escalera
  • Oriol Pujol
  • Petia Radeva
Original Paper


Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success.


Multi-class classification Error correcting output codes Embedding of dichotomizers Object recognition Traffic sign classification Adaboost 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Piccioli G., Micheli E., Campani M.: A robust method for road sign detection and recognition. ECCV 1, 495–500 (1996)Google Scholar
  2. 2.
    Loy, G., Zelinsky, A.: Fast radial symmetry for detecting points of interest. IEEE Trans Pattern Analysis and Machine Intelligence, 8 (2003)Google Scholar
  3. 3.
    Shaposhnikov, D., Podladchikova, L., Golovan, A., Shevtsova, N., Hong, K., Gao, X.: Road sign recognition by single positioning of space-variant sensor (2002)Google Scholar
  4. 4.
    Hsu S., Huang C.: Road sign detection and recognition using matching pursuit method. Image Vis. Comput. 19, 119–129 (2001)CrossRefGoogle Scholar
  5. 5.
    Handmann, U., Kalinke, T., Tzomakas, C., Werner M., von Seelen, W.: An image processing system for driver assistance. IEEE International Conference on Intelligent Vehicles, pp. 481–486 (1998)Google Scholar
  6. 6.
    Casacuberta, J., Miranda, J., Pla, M., Sanchez, S., Serra, A., Talaya, J.: On the accuracy and performance of the geomobil system. Society for Photogrammetry and Remote Sensing (2004)Google Scholar
  7. 7.
    Allwein E., Schapire R., Singer Y.: Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1, 113–141 (2002)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Nilsson N.: Learning Machines. McGraw-Hill, New York (1965)zbMATHGoogle Scholar
  9. 9.
    Dietterich T., Bakiri G.: Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2, 263–286 (1995)zbMATHGoogle Scholar
  10. 10.
    Pujol O., Radeva P., Vitrià J.: Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1007–1012 (2006)CrossRefGoogle Scholar
  11. 11.
    Escalera S., Pujol O., Radeva P.: ECOC-ONE: a novel coding and decoding strategy. Int. Conf. Pattern Recogn. 3, 578–581 (2006)Google Scholar
  12. 12.
    Viola, P., Jones, M.: Robust real-time object detection. Int. J. Comput. Vis. (2002)Google Scholar
  13. 13.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Technical Report (1998)Google Scholar
  14. 14.
    Baro, X., and Vitrià, J.: Traffic sign detection on greyscale images. CCIA, pp. 209–216 (2004)Google Scholar
  15. 15.
    Morse, B.: Segmentation (edge based, hough transform). Technical report (2000)Google Scholar
  16. 16.
    Loy, G., Zelinsky, A.: Fast radial symmetry for detecting points of interest. IEEE Trans. Pattern Anal. Mach. Intell. 25 (2003)Google Scholar
  17. 17.
    Weickert, J.: Anisotropic diffusion in image processing. European Consortium for Mathematics in Industry. B.G. Teubner, Stuttgart (1998)Google Scholar
  18. 18.
    Lowe D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 20, 91–110 (2003)Google Scholar
  19. 19.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository []. University of California, Department of Information and Computer Science, Irvine (2007)
  20. 20.
    Schapire R., Singer Y.: Improved boosting algorithms using confidence-rated prediction. Mach. Learn. 37(3), 297–336 (1999)zbMATHCrossRefGoogle Scholar
  21. 21.
    Zhu, J., Rosset, S., Zou, H., Hastie, T.: Multi-class Adaboost. A multiclass generalization of the Adaboost algorithm, based on a generalization of the exponential loss (2005)Google Scholar
  22. 22.
    Rifkin R., Klautau A.: In defense of one-vs-all classification. J. Mach. Learn. Res. 5, 101–141 (2004)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Department Ciències de la ComputacióComputer Vision Center, UABBellaterraSpain
  2. 2.Department Matemàtica Aplicada i AnàlisiUBBarcelonaSpain

Personalised recommendations