Real time detection and recognition of traffic lights using component subtraction and detection masks

성분차 색분할과 검출마스크를 통한 실시간 교통신호등 검출과 인식

  • 정준익 (전북대학교 전기공학과) ;
  • 노도환 (전북대학교 전자정보공학부)
  • Published : 2006.03.01


The traffic lights detection and recognition system is an essential module of the driver warning and assistance system. A method which is a color vision-based real time detection and recognition of traffic lights is presented in this paper This method has four main modules : traffic signals lights detection module, traffic lights boundary candidate determination module, boundary detection module and recognition module. In traffic signals lights detection module and boundary detection module, the color thresholding and the subtraction value of saturation and intensity in HSI color space and detection probability mask for lights detection are used to segment the image. In traffic lights boundary candidate determination module, the detection mask of traffic lights boundary is proposed. For the recognition module, the AND operator is applied to the results of two detection modules. The input data for this method is the color image sequence taken from a moving vehicle by a color video camera. The recorded image data was transformed by zooming function of the camera. And traffic lights detection and recognition experimental results was presented in this zoomed image sequence.

교통신호등 검출과 인식 시스템은 운전자에게 경고와 보조시스템으로 필요한 장치이다. 본 논문에서는 칼라 비젼시스템을 이용한 주행중 실시간 교통신호등의 검출과 인식법에 대해 제안하고 있다. 제안하는 방법은 크게 네 가지로 구분된다 유사색 환경에서도 신호등 빛 검출이 용이하도록 HSI 색 공간에서 채도와 밝기값의 차를 이용하여 신호등의 빛을 검출하는 신호등 검출, 신호등 외곽검출과 검출된 신호 빛을 바탕으로 교통신호등 외곽 후보영역 설정과 세 검출 결과를 토대로 교통신호등을 인식하는 부분이다. 주행중 영상을 비디오 카메라로 녹화하여 제안하는 방법에 적용하여 결과를 제시하였다. 녹화시 카메라의 줌기능을 이용하여 줌에 의한 입력 영상변화시에도 신호등을 검출 및 인식한 결과를 제시하였다.


  1. U. Franke, D. Gavrila, S. Goerzig, F. Lindner, F. Paetzold, C. Woehler, 'Autonomous Driving Goes Downtown', IEEE Intelligent Systems, Vol. 13, no. 6, pp. 40-48, 1998
  2. Zhuowen Tu and Ron Li, 'Automatic recognition of civil infrastructure objects in mobile mapping imagery using a markov random field model', ISPRS vol. XXXIII, Amsterdam, 2000
  3. Michael Shneier, 'Road Sign Detection and Recognition', IEEE Computer Society International Conference on Computer Vision and Pattern Recognition, June 2005
  4. Blancard, M., 'Road Sign Recognition: A Study of Vision-based decision making for road environment recognition', in Vision-based Vehicle Guidance, pp. 167-175, Springer-Verlag, 1992
  5. Piccioli, G., et al., 'Robust Road Sign Detection and Recognition fromm Image Sequences', Intelligent Vehicles Symposium, pp.278-283, Paris, 1994
  6. Gonzalez and Woods, Digital Image Processing, Addison-Wesley Publishing Company, pp. 229-237, 2002
  7. Bartneck, N. and Ritter, W. 'Color Segmentation with Polynomial Classification', 11th International Conference on Pattern Recognition, vol. 2, pp. 635-638, 1992
  8. Ritter, W., 'Traffic Sign Recognition in Color Image Sequences', Intelligent Vehicles Symposium, pp. 12-17, Detroit, 1992
  9. Estable, S., et al., 'A Real Time Traffic Sign Recognition System', Intelligent Vehicles Symposium, pp. 213-218, Paris, 1994
  10. Escalera, A., Moreno, L., et al., 'Road Traffic Sign Detection and Classification', in IEEE Transactions on Industrial Electronics, Vol.44, pp. 848-858, 1997
  11. Frank L., Ulrich K. and Stephan K., 'Robust Recognition of Traffic Signals', IEEE Intelligent Vehicles Symposium, pp. 49-53, Parma, Italy, June 2004
  12. C. Woehler, J. Anlauf, 'Real-time object recognition on image sequences with the adaptable time delay neural network algorithm applications for autonomous vehicles', Image and Vision Computing, Vol. 19, no. 9-10, pp. 593-618, 2001
  13. R. Duda, P. Hart, D. Stork, Pattern Classification, John Wiley & Sons, 2001
  14. S. Wender, O. Loehlein, 'A Cascade Detector Approach Applied to Vehicle Occupant Monitoring with an Omni-directional Camera', IEEE Intelligent Vehicles Symposium, Parma, 2004
  15. Paul Viola and Michael Jones, 'Robust Real-Time Object Detection', Second International Workshop on Statistical and Computational Theories of Vision-Modeling, Learning, Computing and Sampling, Vancouver, Canada, July 13, 2001