Transactions of the Korean Society of Automotive Engineers (한국자동차공학회논문집)

The Korean Society of Automotive Engineers (한국자동차공학회)
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Real Time Traffic Signal Recognition Using HSI and YCbCr Color Models and Adaboost Algorithm

HSI/YCbCr 색상모델과 에이다부스트 알고리즘을 이용한 실시간 교통신호 인식

  • Park, Sanghoon (Department of Industrial Engineering, College of Engineering, Chonnam National University) ;
  • Lee, Joonwoong (Department of Industrial Engineering, College of Engineering, Chonnam National University)
  • 박상훈 (전남대학교 산업공학과) ;
  • 이준웅 (전남대학교 산업공학과)
  • Received : 2015.08.31
  • Accepted : 2015.11.28
  • Published : 2016.03.01
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Abstract

This paper proposes an algorithm to effectively detect the traffic lights and recognize the traffic signals using a monocular camera mounted on the front windshield glass of a vehicle in day time. The algorithm consists of three main parts. The first part is to generate the candidates of a traffic light. After conversion of RGB color model into HSI and YCbCr color spaces, the regions considered as a traffic light are detected. For these regions, edge processing is applied to extract the borders of the traffic light. The second part is to divide the candidates into traffic lights and non-traffic lights using Haar-like features and Adaboost algorithm. The third part is to recognize the signals of the traffic light using a template matching. Experimental results show that the proposed algorithm successfully detects the traffic lights and recognizes the traffic signals in real time in a variety of environments.

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References

  1. J. I. Jeong and D. H. No, "Real Time Detection and Recognition of Traffic Light Using Component Subtraction and Detection Masks," Journal of the Institute of Electronics and Information Engineers, Vol.43, No.2, pp.65-72, 2006.
  2. M. Shneier, "Road Sign Detection and Recognition," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.
  3. U. Franke, D. Gavrila, S. Goerzig, F.Lindner, F. Paetzold and C. Woehler, "Autonomous Driving Goes Downtown," IEEE Intelligent Systems, Vol.13, No.6, pp.40-48, 1998.
  4. J. B. Kim, "Traffic Lights Detection Based on Visual Attention and Spot-Lights Regions Detection," Journal of the Institute of Electronics and Information Engineers, Vol.51, No.6, pp.132-142, 2014. https://doi.org/10.5573/ieie.2014.51.6.132
  5. J. B. Kim, "Detection of Visual Attended Regions in Road Images for Assisting Safety Driving," Journal of the Institute of Electronics Engineers, Vol.49SC, No.1, pp.94-102, 2012.
  6. R. C. Gonzalez, and R. E. Woods, Digital Image Processing, Addison-Wesley, New York, 1992.
  7. LED 교통신호등 표준지침, 경찰청, 2011.
  8. P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.511-518, 2001.
  9. G. Y. Song, K. Y. Lee and J. W. Lee, "Vehicle Detection Using Edge Analysis and Adaboost Algorithm," Transactions of KSAE, Vol.17, No.1, pp.1-11, 2009.
  10. Wikipedia, Cross-correlation, https://en.wikipedia.org/wiki/Cross-correlation#Normalized_cros s-correlation, 2015.
  11. R. Y. Tasi, "A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the Shelf TV Cameras and Lenses," IEEE J. Robotics and Automation, Vol.3, No.4, pp.323-344, 1987. https://doi.org/10.1109/JRA.1987.1087109