Advertisement

Committee Machine for Road-Signs Classification

  • Bogusław Cyganek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)

Abstract

The paper presents a system for the road signs recognition which is based on an ensemble of the non Euclidean distance neural networks and an arbitration unit. The input to this system constitutes a binary pictogram of a sign which is supplied from the detection module. The classifier is composed of a mixture of experts – the Hamming neural networks – each working with a single group of deformed reference pictograms. The ensemble of experts is controlled by an arbitration module operating in the winner-takes-all mode. Additionally it is equipped with a promoting mechanism that favours the most populated group of unanimous experts. The presented classifier is characterized by the fast training and very fast response times which features make it suitable for the driving assistant systems. The presented concepts have been verified experimentally. Their results and conclusions are also discussed.

Keywords

Input Pattern Road Sign Driving Assistant System Winning Neuron Binary Distance 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aoyagi, Y., Asakura, T.: A study on traffic sign recognition in scene image using genetic algorithms and neural networks. In: IEEE Conf. Electronics, pp. 1838–1843 (1996)Google Scholar
  2. 2.
    Chen, X., Yang, J., Zhang, J., Waibel, A.: Automatic Detection and Recognition of Signs From Natural Scenes. IEEE Trans. on Image Proc. 13(1), 87–99 (2004)CrossRefGoogle Scholar
  3. 3.
    Cyganek, B.: Object Detection in Multi-Channel and Multi-Scale Images Based on the Structural Tensor. In: Gagalowicz, A., Philips, W. (eds.) CAIP 2005. LNCS, vol. 3691, pp. 570–578. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Duch, W., Grudziński, K.: A framework for similarity-based methods. In: Second Polish Conference on Theory and Applications of Artificial Intelligence, pp. 33–60 (1998)Google Scholar
  5. 5.
    Escalera, A., Moreno, L., Salichs, M., Armingol, J.: Road traffic sign detection and classification. IEEE Trans. Ind. Electron. 44, 848–859 (1997)CrossRefGoogle Scholar
  6. 6.
    Escalera, A., Armingol, J.: Visual Sign Information Extraction and Identification by Deformable Models. IEEE Tr. On Int. Transportation 5(2), 57–68 (2004)CrossRefGoogle Scholar
  7. 7.
    Floréen, P.: Computational Complexity Problems in Neural Associative Memories. PhD Thesis, University of Helsinki, Finland (1992)Google Scholar
  8. 8.
    Lippman, R.: An introduction to computing with neural nets. IEEE Transactions on Acoustic, Speech, and Signal Processing ASSP-4, 4–22 (1987)Google Scholar
  9. 9.
    Luo, R., Potlapalli, H., Hislop, D.: Neural network based landmark recognition for robot navigation. In: IEEE Int. Conf. Industrial Electronics pp. 1084–1088 (1992)Google Scholar
  10. 10.
    Luo, R., Potlapalli, H.: Landmark recognition using projection learning for mobile robot navigation. In: Proc. IEEE Int. Conf. Neural Networks, pp. 2703–2708 (1994)Google Scholar
  11. 11.
    Piccioli, G., Micheli, E.D., Parodi, P., Campani, M.: Robust method for road sign detection and recognition. Image and Vision Computing 14, 209–223 (1996)CrossRefGoogle Scholar
  12. 12.
    Road Signs and Signalization. Directive of the Polish Ministry of Infrastructure. Internal Affairs and Administration (Dz. U. Nr 170, poz. 1393) (2002)Google Scholar
  13. 13.
    Rehrmann, V., Lakmann, R., Priese, A.: A parallel system for real-time traffic sign recognition. Deimler-Benz Technical Report (1995)Google Scholar
  14. 14.
    Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 151-158 (2001)Google Scholar
  15. 15.
    Zheng, Y.J., Ritter, W., Janssen, R.: An adaptive system for traffic sign recognition. In: Proc. IEEE Intelligent Vehicles Symp., pp. 165–170 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bogusław Cyganek
    • 1
  1. 1.AGH – University of Science and TechnologyKrakówPoland

Personalised recommendations