single-jc.php

JACIII Vol.10 No.3 pp. 409-418
doi: 10.20965/jaciii.2006.p0409
(2006)

Paper:

An Efficient Algorithm for Traffic Sign Detection

Aryuanto Soetedjo*, and Koichi Yamada**

*Information Science and Control Engineering, Nagaoka University of Technology, 1603-1 Kamitomiokamachi, Nagaoka, Niigata 940-2188, Japan

**Management and Information Systems Science, Nagaoka University of Technology, 1603-1 Kamitomiokamachi, Nagaoka, Niigata 940-2188, Japan

Received:
July 15, 2005
Accepted:
October 21, 2005
Published:
May 20, 2006
Keywords:
traffic sign detection, ellipse detection, geometric fragmentation, genetic algorithm, objective function
Abstract

We propose an efficient algorithm for detecting traffic signs in images. Geometric fragmentation detects circular red traffic signs in an image by finding and combining the left and right fragments of elliptical objects to increase the accuracy of detection and cope with occlusion. The search for fragments resembles a genetic algorithm (GA) in that it uses the terms individual, population, crossover, and objective function used in the GA. It is different in that it conducts a concurrent random search in a small two-dimensional space devised heuristically. The objective function for evaluating individuals is devised to increase detection accuracy and reduce computation time. The algorithm was tested for detecting circular red traffic signs both from artificial sign images and real scene images. Experimental results demonstrated that the proposed algorithm has higher detection, fewer false alarms, and lower computation cost than GA-based ellipse detection. Compared to conventional template matching, the proposed algorithm performs better in detection and execution time and does not require a large number of carefully prepared templates.

Cite this article as:
Aryuanto Soetedjo and Koichi Yamada, “An Efficient Algorithm for Traffic Sign Detection,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.3, pp. 409-418, 2006.
Data files:
References
  1. [1] H. M. Yang, C. L. Liu, and S. M. Huang, “Traffic sign recognition in disturbing Environments,” Proc. of ISMIS’03, Maebashi City, Japan, pp. 28-31, October, 2003.
  2. [2] J. C. Hsien, and S. Y. Chen, “Road sign detection and recognition using markov model,” The 14th Workshop on OOTA, 2003.
  3. [3] S.-H. Hsu, and C.-L. Huang, “Road sign detection and recognition using matching pursuit method,” Image and Vision Computing, Vol.19, pp. 119-129, 2001.
  4. [4] D. M. Gavrila, “Traffic sign recognition revisited,” Proc. of the 21st DAGM Symposium fur Musterekennung, pp. 86-93, Springer Verlag, 1999.
  5. [5] L. Sekanina, and J. Torresen, “Detection of Norwegian speed limit signs,” Proc. of the 16th European Simulation Multiconference, Delft, NL, SCS, pp. 337-340, 2002.
  6. [6] Y. Aoyagi, and T. Asakura, “A study on traffic sign recognition in scene image using genetic algorithms and neural networks,” Proc. of 22nd International Conference on Industrial Electronics, Control, and Instrumentation, IEEE, August, 1996.
  7. [7] A. de la Escalera, J. M. Armingol, and M. Mata, “Traffic sign recognition and analysis for intelligent vehicles,” Image and Vision Computing, Vol.21, pp. 247-258, 2003.
  8. [8] G. Roth, and M. D. Levine, “Geometric primitive extraction using genetic algorithm,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.16, 1994.
  9. [9] P. Y. Yin, “A new circle/ellipse detector using genetic algorithms,” Pattern Recognition Letter, Vol.20, 1999.
  10. [10] H. Chun-Ta, and C. Ling-Hwei, “A high speed algorithm for elliptical object detection,” IEEE Transactions on Image Processing, Vol.5, No.3, March, 1996.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 11, 2021