OJAppS  Vol.3 No.1 B , March 2013
Robust Lane Detection in Shadows and Low Illumination Conditions using Local Gradient Features
Abstract: This paper presents a method for lane boundaries detection which is not affected by the shadows, illumination and un-even road conditions. This method is based upon processing grayscale images using local gradient features, characteris-tic spectrum of lanes, and linear prediction. Firstly, points on the adjacent right and left lane are recognized using the local gradient descriptors. A simple linear prediction model is deployed to predict the direction of lane markers. The contribution of this paper is the use of vertical gradient image without converting into binary image(using suitable thre-shold), and introduction of characteristic lane gradient spectrum within the local window to locate the preciselane marking points along the horizontal scan line over the image. Experimental results show that this method has greater tolerance to shadows and low illumination conditions. A comparison is drawn between this method and recent methods reported in the literature.
Cite this paper: A. Parajuli, M. Celenk and H. Riley, "Robust Lane Detection in Shadows and Low Illumination Conditions using Local Gradient Features," Open Journal of Applied Sciences, Vol. 3 No. 1, 2013, pp. 68-74. doi: 10.4236/ojapps.2013.31B014.

[1]   Y. Wang, N. Dahnoun, and A. Achim, “A novel system for robust lane detection and tracking,” Signal Processing, vol. 92, no. 2, pp. 319–334, 2012. View at Publish-er ? View at Google Scholar.

[2]   Z. Kim, “Robust lane detection and tracking in challenging scenarios,” IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 1, pp. 16–26, 2008.view.

[3]   C. Lipski “A fast and robust approach to lane marking detection and lane tracking” in proceedings of IEEE SSIAI, 24-26 march 2008.view

[4]   C. Rasmussen,”Groupingdominat structure of Ill structured Road Following” in proceedings of IEEE computer society, CVPR 2004 27 June-2 July 2004, pages I-470 - I-477 Vol.1

[5]   S. Sivaraman and M. M. Trivedi “Improved vision-based lane tracker performance using vehicle localization” in proceedings of IEEE intelligent vehicles symposium, June 21-24, 2010. view

[6]   A. Borkar “A layered approach to robust lane detection at night” in proceedings of IEEE CIVVS, March 30-April 2 , 2009

[7]   Y. Fan, W. Zhang, X. Li, L. Zhang, and Z. Cheng “A robust lane bounda-ries detection algorithm based on gradient distribution features,” Proceedings of the 8th International conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp 1714-1718, July 2011.

[8]   J. Wang, F. Gu, C. Zhang, and G. Zhang “Lane boundary detection based on parabola method,” Proceedings of the 2010 IEEE, International conference on Information and Automation, pp 1729-1734, china, June 2010.view

[9]   J. C. McCall and M. M. Trivedi, “Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation,” IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 1, pp. 20–37, 2006.

[10]   A. Bar Hillel, R. Lerner, D. Levi, and G. Raz, “Recent progress in road and lane detection: a survey,” Machine Vision and Applications. In press. View at Publisher ? View at Google Scholar

[11]   J McDonald. “Detecting and tracking road markings using the Hough transform,” Proc. Of the Irish Machine Vision and Image Processing Conference 2001.

[12]   P. L. Palmer, J. Kittler and M. Petrou, “An optimizing line finder using a Hough transform algorithm,” Computer Vision and Image Understanding, vol. 68, no 1, pp. 1-23, July 1993.

[13]   K. Kluge and S. Lakshmanan, “A deformable-template approach to lane detection,” Proceedings of the Intelligent Vehicles '95 Symposium, pp. 54-59, September 1995

[14]   Y. Wang, et al.,” Lane detection using spline model,” Pattern Recognition Letters, vol 21, pp.677-689, 2000.

[15]   Y. Wang, et al.,” Lane detection and tracking using B-Snake,” Image and Vision Computing, vol. 22, pp.269-280, 2004.

[16]   J. W. Park, et al., “A lane-curve detection based on LCF,” Pattern Recognition Letters, vol 24, pp.2301-2313, 2003. view

[17]   Dong-Joongkang and Mun-Ho Jung,”Road lane segmentation using dynamic programming for active safety vehicles,” Pattern Recognition Letters, vol 24, pp 3177-3185, July 2003.

[18]   S.P.Liou, R.C. Jain, “Road following using vanishing points,“ Computer Vision, Graphics, and Im-age Processing 39 (1987) 116-130.

[19]   S. Lakshmanan and K. Kluge, “LOIS: A real-time lane detection algo-rithm,” in Proceedings 30th Annual Conference of In-formation Science Systems, 1996, pp.1007–1012.

[20]   C. Kreucher and S. Lakshmanan, “LANA: A lane extraction algorithm that uses frequency domain features”, IEEE Transactions on Robotics and Automation, vol. 15, no. 2, pp.343-350, April 1999.

[21]   J.S. Lim, Two Dimensional Signal and Image Processing, Prentice Hall 1990.

[22]   S. Theodoridis and K. Koutroumbas, Pattern Recognition, 3rd ed. New York: Academic, Feb. 2006.

[23]   Carnegie-Mellon-University, “CMU/VASC image database1997–2003,” link

[24]   Carlos.Aguilera, “Finding local Extrema in Matlab” (, link to matlab files