DOI QR코드

DOI QR Code

Traffic Sign Recognition Considering the Intensity of Illumination

조도를 고려한 표지판 인식

  • 차연화 (연세대학교 컴퓨터과학과) ;
  • 전창묵 (한국과학기술연구원 인지로봇센터) ;
  • 권태범 (한국과학기술연구원 인지로봇센터) ;
  • 강성철 (한국과학기술연구원 인지로봇센터)
  • Received : 2011.01.21
  • Accepted : 2011.05.04
  • Published : 2011.05.31

Abstract

Recognition of traffic signs helps an unmanned ground vehicle to decide its behavior correctly, and it can reduce traffic accidents. However, low cost traffic sign recognition using a vision sensor is very difficult because the signs are exposed to various illumination conditions. This paper proposes a new approach to solve this problem using an illuminometer which detects the intensity of illumination. Using the intensity of illumination, the recognizer adjusts the parameters for image processing. Therefore, we can reduce the loss of information such as the shape and color of traffic signs. Experimental results show that the proposed method is able to improve the performance of traffic sign recognition in various weather and lighting conditions.

Keywords

References

  1. J.‐M. Armingol, J.‐P. Carrasco, and J.‐M. Collado, Autonomous Robots Research Advances, Nova Science Publishers, 2008.
  2. J.‐P. Carrasco, "Advanced Driver Assistance System based on Computer Vision using Detection, Recognition and Tracking of Road Sign", thesis doctoral, Universidad Carlos III DE Madrid, 2009.
  3. W. G. Shadeed, D. I. Abu‐Al‐Nadi, and M. J. Mismar," Road traffic sign detection in color images", Proc. of IEEE International Conference on Electrics, Circuits, andSystems, Sharjah, United Arab Emirates, 2003.
  4. H. Fleyeh, "Shadow and Highlight Invariant Colour Segmentation Algorithm for Traffic Signs", Proc. of IEEE International Conference on Cybernetics and Intelligent Systems, Bangkok, Thailand, 2006.
  5. C. Bahlmann, Y. Zhu, and V. Ramesh. "A system for Traffic Sign Detection, Tracking, and Recognition Using Color, Shape, and Motion Information" Proc. of Intelligent Vehicles Symposium, pp 255‐260, 2005.
  6. D‐L. 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.
  7. M. HuAn, S. Zhu and K. Chen. "An Effective Method for Traffic Signs Segmentation", Proc. of IEEE International Conference on Intelligent Human System and Cybernetics, 2009.
  8. C. Liao, T‐T Zin, T. Kaneko and H. Hama, "Robust segmentation of road traffic signs using adaptive thresholds", IEICE Electronics Express, 2005.
  9. H. Fleyeh, "Traffic and Road Sign Recognition", thesis doctoral, Napier University, 2008.
  10. D.‐H. Kim, M.‐K. Kang, and E.‐Y. Cha, "퍼지 멤버쉽 함수로 최적화된 LVQ를 이용한 패턴 분류 모델", 정보과학회논문지, Vol.29, pp 573‐583, 2002.

Cited by

  1. An Experimental Study on Crack Recognition Characteristics of Concrete Structure based on Image Analysis according to Illuminance and Measurement Distance vol.14, pp.1, 2014, https://doi.org/10.9798/KOSHAM.2014.14.1.85