Improvement of a Traffic Sign Detector by Retrospective Gathering of Training Samples from In-Vehicle Camera Image Sequences

  • Daisuke Deguchi
  • Keisuke Doman
  • Ichiro Ide
  • Hiroshi Murase
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6469)


This paper proposes a method for constructing an accurate traffic sign detector by retrospectively obtaining training samples from in-vehicle camera image sequences. To detect distant traffic signs from in-vehicle camera images, training samples of distant traffic signs are needed. However, since their sizes are too small, it is difficult to obtain them either automatically or manually. When driving a vehicle in a real environment, the distance between a traffic sign and the vehicle shortens gradually, and proportionally, the size of the traffic sign becomes larger. A large traffic sign is comparatively easy to detect automatically. Therefore, the proposed method automatically detects a large traffic sign, and then small traffic signs (distant traffic signs) are obtained by retrospectively tracking it back in the image sequence. By also using the retrospectively obtained traffic sign images as training samples, the proposed method constructs an accurate traffic sign detector automatically. From experiments using in-vehicle camera images, we confirmed that the proposed method could construct an accurate traffic sign detector.


Training Sample Sign Detector Face Detection Sign Image Traffic Sign 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Maldonado-Bascón, S., Lafuente-Arroyo, S., Gil-Jiménez, P., Gómez-Moreno, H., López-Ferreras, F.: Road-sign detection and recognition based on support vector machines. IEEE Transactions on Intelligent Transportation Systems 8(2), 264–278 (2007)CrossRefzbMATHGoogle Scholar
  2. 2.
    Loy, G., Barnes, N.: Fast shape-based road sign detection for a driver assistance system. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 70–75 (2004)Google Scholar
  3. 3.
    Bahlmann, C., Zhu, Y., Ramesh, V., Pellkofer, M., Koehler, T.: A system for traffic sign detection, tracking, and recognition using color, shape, and motion information. In: Proceedings of IEEE Intelligent Vehicles Symposium, pp. 255–260 (2005)Google Scholar
  4. 4.
    Doman, K., Deguchi, D., Takahashi, T., Mekada, Y., Ide, I., Murase, H.: Construction of cascaded traffic sign detector using generative learning. In: Proceedings of International Conference on Innovative Computing Information and Control, ICICIC-2009-1362 (2009)Google Scholar
  5. 5.
    Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  6. 6.
    Wöhler, C.: Autonomous in situ training of classification modules in real-time vision systems and its application to pedestrian recognition. Pattern Recognition Letters 23(11), 1263–1270 (2002)CrossRefzbMATHGoogle Scholar
  7. 7.
    Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(8), 790–799 (2005)CrossRefGoogle Scholar
  8. 8.
    Coope, I.D.: Circle fitting by linear and nonlinear least squares. Journal of Optimization Theory and Applications 76(2), 381–388 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Burghouts, G.J., Geusebroek, J.M.: Performance evaluation of local colour invariants. Computer Vision and Image Understanding 113(1), 48–62 (2009)CrossRefGoogle Scholar
  10. 10.
    Hradis, M., Herout, A., Zemcik, P.: Local rank patterns – novel features for rapid object detection. In: Bolc, L., Kulikowski, J.L., Wojciechowski, K. (eds.) ICCVG 2008. LNCS, vol. 5337, pp. 239–248. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3), 297–336 (1999)CrossRefzbMATHGoogle Scholar
  12. 12.
    Huang, C., Ai, H., Wu, B., Lao, S.: Boosting nested cascade detector for multi-view face detection. In: Proceedings of the International Conference on Pattern Recognition, vol. 2, pp. 415–418 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Daisuke Deguchi
    • 1
  • Keisuke Doman
    • 1
  • Ichiro Ide
    • 1
  • Hiroshi Murase
    • 1
  1. 1.Graduate School of Information ScienceNagoya UniversityNagoyaJapan

Personalised recommendations