수평 1-D LoG 필터링 스케일 공간과 가변적 문턱처리의 결합에 의한 차선 마킹 검출 개선

Improving Lane Marking Detection by Combining Horizontal 1-D LoG Filtered Scale Space and Variable Thresholding

  • 유현중 (상명대학교 정보통신공학과)
  • 투고 : 2012.05.25
  • 심사 : 2012.06.15
  • 발행 : 2012.07.25

초록

차선 마킹 검출은 지능형 운송 시스템(ITS, intelligent transportation systems), 운전자 보조 시스템(DAS, driver assistant systems) 등에 필수적인 요소이다. 이 논문에서는 스케일 공간 기법을 이용하여 기존의 기법들에 비해 견고한 차선 마킹 검출기법을 제안한다. 차선 마킹 검출에 많이 사용되고 있는 지역 통계 기반 가변적 문턱처리 기법은 밝기 특성이 두드러진 객체의 검출에 유리하므로 차선 마킹 검출에 효과적일 수 있다. 그러나 통계적 특징만으로는 무관한 영역도 함께 검출되므로, 이 논문에서는 가변적 문턱처리 결과와 함께 수평 1D LoG 필터링 스케일 공간을 합성하여 차선 마킹 후보 영역을 축소하는 기법을 제안한다. 실제 영상에 대해 가변적 문턱처리뿐만 아니라 차선 마킹 검출을 위한 또 다른 대표적인 기법인 하프 변환을 사용하는 기법과도 비교한 결과, 뚜렷한 차선 마킹 후보 영역 축소를 확인할 수 있었다.

Lane marking detection is essential to both ITS and DAS systems. The objective of this paper is to provide more robust technique for lane marking detection than traditional techniques by using scale-space technique. Variable thresholding that is based on the local statistics may be very effective for detecting such objects as lane markings that have prominent intensities. However, such techniques that only rely on local statistics have limitations containing irrelevant areas as well. We reduce the candidate areas by combining the variable thresholding result with cost-efficient horizontal 1D LoG filtered scale space. Through experiments using practical images, we could achieve significant improvement over the techniques based not only on the variable thresholding but also on the Hough transform that is another very popular technique for this purpose.

키워드

참고문헌

  1. J.Crisman, "UNSCARF, a color vision system for the detection of unstructured roads," Proceedings of IEEE International Conference on Robotics and Automation, Sacramento, California, pp. 2496-2501, Apr 1991.
  2. D. Dahyot, "Statistical Hough transform", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 31, no. 8, pp. 1502-1509, 2009. https://doi.org/10.1109/TPAMI.2008.288
  3. 김기석, 이진욱, 조재수, "허프 변환과 차선 모델을 이용한 효과적인 차선검출에 관한 연구", 대한전자공학회 2009년도 정보및제어심포지움, pp. 34-36, 2009.
  4. 이상영, 박래홍, "Hough Transform을 이용한 지식 기반 차선검출", 대한전자공학회 2003년도 하계종합학술대회, pp. 1988-1991, 2003.
  5. K. Kluge, "Extracting road curvature and orientation from image edge points without perceptual grouping into features," Proceedings of IEEE Intelligent Vehicles Symposium, pp. 109-114, 1994.
  6. A. Broggi and S. Berte, "Vision-based road detection in automotive systems: A real-time expectation-driven approach," Journal of Artificial Intelligence Research, pp.325-348, 1995.
  7. B. Yu and A. Jain, "Lane boundary detection using a multiresolution hough transform," Proceedings of International Conference on Image Processing, pp.748-75, vol. 1, 26-29 Oct 1997.
  8. B. Southall and C. J. Taylor, " Stochastic road shape estimation," Proceedings of IEEE International Conference on Computer Vision, Vancouver, BC, Canada, July 2001.
  9. Q. Chen and H. Wang, "A Real-time Lane Detection Algorithm based on a Hyperbola-Pair Model", Intelligent Vehicles Symposium, pp. 510-515, Tokyo, Japan, June 13-15, 2006.
  10. Marcos Nieto . Jon Arrospide Laborda . Luis Salgado, "Road environment modeling using robust perspective analysis and recursive Bayesian segmentation", Machine Vision and Applications, vol. 22, pp. 927-945, 2011. https://doi.org/10.1007/s00138-010-0287-7
  11. M. Kazui, M. Haseyama, and H. Kitajima, "The estimation of the vanishing point for automatic driving systems using a cross ratio," Systems and Computers in Japan, pp. 31-40, 2001.
  12. M. Concel, Detection and tracking of vanishing points in dynamic environments, Ph.D. Thesis, 2010.
  13. C. Kreucher and S. Lakshmanan, "LANA: a lane extraction algorithm that uses frequency domain features," IEEE Transactions on Robotics and automation, pp. 343-350, Apr 1999.
  14. D. Guru, B. Shekar, A simple and robust line detection algorithm based on small eigenvalue analysis, Pattern Recognition Letters, vol. 25, no. 1, pp. 1-13, 2004. https://doi.org/10.1016/j.patrec.2003.08.007
  15. J. Crisman and C. Thorpe. "SCARF: A color vision system that tracks roads and intersections", Robotics and Automation, vol. 9, no. 1: 49-58, February 1993. https://doi.org/10.1109/70.210794
  16. A. K. Dawoud, S. G. Foda, and A. S. Tolba, ''A robust neural network multi-lane recognition system," Proceedings of the 12th International Conference on Microelectronics, pp. 178-182, 14-16 Dec 1998.
  17. R. Risack, N. Mohler, and W. Enkelmann, "A video-based lane keeping assistant," Proceedings of IEEE Intelligent Vehicles Symposium, pp. 356-36, vol. 1, 3-5 Oct 2000.
  18. http://www.mobileye.com
  19. T. Lindberg, "Scale-space", Encyclopedia of Computer Science and Engineering (Benjamin Wah, ed), John Wiley and Sons, vol. IV, pp. 2495-2504, Hoboken, New Jersey, 2009.
  20. R. Gonzalez and R. Woods, Digital Image Processing, 2ed., Pearson Eduction Inc., 2008.