Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic


eISSN: 1178-5608



VOLUME 10 , ISSUE 3 (September 2017) > List of articles


Aryuanto Soetedjo * / I Komang Somawirata *

Keywords : HOG, traffic sign classification, ring partitioned, template matching.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 10, Issue 3, Pages 735-753, DOI:

License : (CC BY-NC-ND 4.0)

Received Date : 02-June-2017 / Accepted: 27-July-2017 / Published Online: 01-September-2017



This paper presents a technique to classify the circular traffic sign based-on HOG (histogram of oriented gradients) and a ring partitioned matching. The method divides an image into several ring areas, and calculates the HOG feature on each ring area. In the matching process, the weight is assigned to each ring for calculating the distance of HOG feature between tested image and reference image. The experimental results show that the proposed algorithm achieves a high classification rate of 97.8%, without the need of many prepared sample images. The results also show that the best values of the number of orientation bins and the cell size of the HOG parameters are 5 and 10 x 10 pixels respectively.

Content not available PDF Share



  1. A. Soetedjo and I.K. Somawirata, “Implementation of eye detection using dual camera on the embedded system,” International Journal of Innovative Computing, Information and Control (IJICIC), Vol. 13, No. 2, pp. 397-409, 2017.
  2. X. Miao and S.L. Huan Shen, “On-board lane detection system for intelligent vehicle based on monocular vision,” International Journal on Smart Sensing and Intelligent Systems, Vol. 5, No. 4, pp. 957-972, 2012.
  3. S.B. Wali, M.A. Hannan, A. Hussain and S.A. Samad, “An automatic traffic sign detection and recognition system based on colour segmentation, shape matching, and SVM,” Mathematical Problems in Engineering, Vol. 2015, pp. 1-11, 2015.
  4. R. Biswas, H. Fleyeh and M. Mostakim, “Detection and classification of speed limit traffic signs,” Proceedings of 2014 World Congress on Computer Applications and Information Systems (WCCAIS), Hammamet, Tunisia, 2014, pp. 1-6.
  5. A. Adam and C. Ioannidis, “Automatic road-sign detection and classification based on support vector machines and HOG descriptors,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. II-5, pp. 1-7, 2014.
  6. J. Greenhalgh and M. Mirmehdi, “Real-time detection and recognition of road traffic signs,” IEEE Trans. on Intelligent Transportation Systems, Vol. 13, No. 2, pp. 1498-1506, 2012.
  7. N.B. Romdhane, H. Mliki and M. Hammami, “An improved traffic signs recognition and tracking method for driver assistance system,” Proceedings of 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), Okayama, Japan, 2016, pp. 1-6.
  8. M.A. García-Garrido, M. Ocana, D.F. Llorca, E. Arroyo, J. Pozuelo and M. Gavilan, “Complete vision-based traffic sign recognition supported by an I2V communication system,” Sensors, Vol. 12, Issue 2, pp. 1148-1169, 2012.
  9. T. Bui-Minh, O. Ghita, P. F. Whelan and T. Hoang, "A robust algorithm for detection and classification of traffic signs in video data," Proceedings of 2012 International Conference on Control, Automation and Information Sciences (ICCAIS), Ho Chi Minh City, Vietnam, 2012, pp. 108-113.
  10. S.K. Berkaya, H. Gunduz, O. Ozsen, C. Akinlar and S. Gunal, “On circular traffic sign detection and recognition,” Expert Systems with Applications, Vol. 48, pp. 67-75, 2016.
  11. J. Greenhalgh and M. Mirmehdi, “Traffic sign recognition using MSER and random forests,” Proceedings of 20th European Signal Processing Conference (EUSIPCO 2012), Bucharest, Romania, 2012, pp. 1935-1939.
  12. S. Yin, P. Ouyang, L. Liu, Y. Guo and S. Wei, “Fast traffic sign recognition with a rotation invariant binary pattern based feature,” Sensors, Vol. 15, Issue 1, pp. 2161-2180, 2015.
  13. I. Filkovic, “Traffic sign localization and classification methods: an overview,“ [Online]. Available: /repository/ KDI,_Ivan_Filkovic.pdf
  14. M. Peemen, B. Mesman and H. Corporaal, “Speed sign detection and recognition by convolutional neural networks,” Proceedings of 8th International Automotive Congress, Eindhoven, Nederland, 2011, pp. 162–170.
  15. A. Ruta, F. Porikli, S. Watanabe and Y. Li, “In-vehicle camera traffic sign detection and recognition,” Machine Vision and Applications, Vol. 22, pp. 359–375, 2011.
  16. R. Laguna, R. Barrientos, L.F. Blazquez and L.J. Miguel, “Traffic sign recognition application based on image processing techniques,” Proceedings of the 19th World Congress The International Federation of Automatic Control, Cape Town, South Africa. August 24-29, 2014, pp. 104-109.
  17. A. Soetedjo and K. Yamada, “Traffic sign classification using ring-partitioned matching,“ IEICE Trans. Fundamentals, Vol. E88-A, No. 9, pp. 2419-2426, 2005.
  18. R.F. Rachmadi, Y. Komokata, K. Uchimura, and G. Koutaki, “Road sign classification system using cascade convolutional neural network,” International Journal of Innovative Computing, Information and Control, Vol. 13, No. 1, pp. 95-109, 2017.
  19. Z. Huang, Y. Yu, J. Gu and H. Liu, “An efficient method for traffic Sign recognition based on extreme learning machine,” IEEE Transactions on Cybernetics, Vol. 47, Issue 4, pp. 920-933, 2017.
  20. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), Vol. 1, San Diego, CA, USA, 2005, pp. 886-893.
  21. N. Dalal, “Finding people in images and videos,” PhD thesis, INRIA, French, 2006.
  22. J. Stallkamp, M. Schlipsing, J. Salmen and C. Igel. “The German Traffic Sign Recognition Benchmark: A multi-class classification competition,” Proceedings of the IEEE International Joint Conference on Neural Networks, San Jose, CA, USA, 2011, pp. 1453–1460.