Noise and Illumination Invariant Road Detection Based on Vanishing Point

  • Wei Luo
  • Heyou Chang
  • Jian Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7751)


We propose a new method for robust road detection under noise and illumination varying conditions. Original input image is first divided into smooth and detailed component through structure-texture decomposition, where we verify the texture image is robust to various complicated road conditions. The texture image is then be used to compute each pixel’s dominant orientation through Gabor wavelet analysis, followed by generating the vanishing point via grouping voters, which has an orientation confidence larger than a fixed threshold, in corresponding voting region through soft voting. Finally the road borders are constructed by feature inconsistency maximization criterion. Experiments on various road, weather, noise and lighting conditions are justified the accuracy and robust of our method. Furthermore, we analyze the applicability of texture based vanishing point method and conclude the main factors that degenerate the performance of this class method.


road detection vanishing point noise and illumination invariant 


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  1. 1.
    Leonard, J., et al.: A Perception-Driven Autonomous Urban Vehicle. J. Field Robotics 25(10), 727–774 (2008)CrossRefGoogle Scholar
  2. 2.
    Urmson, C., et al.: Autonomous Driving in Urban Environments: Boss and the Urban Challenge, J. Field Robotics 25(8), 425–466 (2008)CrossRefGoogle Scholar
  3. 3.
    Montemerlo, M., et al.: Junior: The Stanford Entry in the Urban Challenge. J. Field Robotics 25(9), 569–597 (2008)CrossRefGoogle Scholar
  4. 4.
    Miller, I., Campbell, M., et al.: Team Cornell’s Skynet: Robust Perception and Planning in an Urban Environment. J. Field Robotics 25(8), 493–527 (2008)CrossRefGoogle Scholar
  5. 5.
    McCall, J.C., Trivedi, M.M.: Video-Based Lane Estimation and Tracking for Driver Assistance: Survey, System, and Evaluation. IEEE TITS 7(1), 20–37 (2006)Google Scholar
  6. 6.
    Wang, Y., Teoh, E.K., Shen, D.: Lane detection and tracking using B-Snake. Image Vision Computing 22, 269–280 (2004)CrossRefGoogle Scholar
  7. 7.
    Caraffi, C., Cattani, S., Grisleri, P.: Off-Road Path and Obstacle Detection Using Decision Networks and Stereo Vision. IEEE TITS 8(4), 607–618 (2007)Google Scholar
  8. 8.
    Bertozzi, M., et al.: Artificial Vision in Road Vehicles. Proceedings of the IEEE 90(7), 1258–1269 (2002)CrossRefGoogle Scholar
  9. 9.
    Kastrinake, V., Zervakis, M., Kalaitzakis, K.: A survey of video processing techniques for traffic applications. Image and Vision Computing 21, 359–381 (2003)CrossRefGoogle Scholar
  10. 10.
    Rasmussen, C.: Grouping Dominant Orientations for Ill-Structured Road Following. In: CVPR (2004)Google Scholar
  11. 11.
    Kong, H., Audibert, J.-Y., Ponce, J.: General Road Detection From a Single Image. IEEE TIP 19(8), 2211–2220 (2010)MathSciNetGoogle Scholar
  12. 12.
    Wedel, A., Pock, T., Zach, C., Bischof, H., Cremers, D.: An Improved Algorithm for TV-L1 Optical Flow. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds.) Statistical and Geometrical Approaches to Visual Motion Analysis. LNCS, vol. 5604, pp. 23–45. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Sun, D., Roth, S., Black, M.J.: Secrets of Optical Flow Estimation and Their Pricinples. In: CVPR (2010)Google Scholar
  14. 14.
    Se, S.: Zebra-crossing Detection for the Partially Sighted. In: CVPR, vol. 2, pp. 211–217 (2000)Google Scholar
  15. 15.
    Simond, N., Rives, P.: Homography from a Vanishing Point in Urban Scenes. In: International Conference on Intelligent Robots and Systems (IROS), vol. 1, pp. 1005–1010 (2003)Google Scholar
  16. 16.
    Coughlan, J.M., Yuille, A.L.: Manhattan World: Orientation and Outlier Detection by Bayesian Inference. Neural Computation 15(5), 1063–1088 (2003)CrossRefGoogle Scholar
  17. 17.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)zbMATHCrossRefGoogle Scholar
  18. 18.
    Chambolle, A.: Total Variation Minimization and a Class of Binary MRF Models. In: Rangarajan, A., Vemuri, B.C., Yuille, A.L. (eds.) EMMCVPR 2005. LNCS, vol. 3757, pp. 136–152. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2003)Google Scholar
  20. 20.
    Lee, T.: Image representation using 2d gabor wavelets. IEEE TPAMI 18(10), 959–971 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wei Luo
    • 1
  • Heyou Chang
    • 1
  • Jian Yang
    • 1
  1. 1.School of Computer ScienceNJUSTNanjingP.R.C.

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