Real-Time Autonomous Colour-Based Following of Ill-Defined Roads

  • Marek Ososinski
  • Frédéric Labrosse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7429)

Abstract

Autonomous following of ill-defined roads is an important part of visual navigation systems. The majority of current image-based road following methods rely on computationally expensive algorithms. This paper presents an adaptive real-time method based on statistical analysis of the colour of a road surface in a trapezoidal shape that corresponds to the projection of the road on the image plane. Our method is capable of navigating in real-time in a variety of situations, including 90°turns and crossroads, and coping with variable conditions of the road such as surface type and shadows.

Keywords

Colour Space Road Surface Colour Component Road Width Trapezoidal Shape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Alvarez, J.M.A., Lopez, A.M.: Road detection based on illuminant invariance. IEEE Transactions on Intelligent Transportation Systems 12, 184–193 (2011)CrossRefGoogle Scholar
  2. 2.
    Bao, J., Chen, Y., Yu, J.: An optimized discrete neural network in embedded systems for road recognition. Engineering Applications of Artificial Intelligence 25(4), 775–782 (2012)CrossRefGoogle Scholar
  3. 3.
    Broggi, A., Cattani, S.: An agent based evolutionary approach to path detection. Special Issue on Evolutionary Computer Vision and Image Understanding, Pattern Recognition Letters 27, 1164–1173 (2006)Google Scholar
  4. 4.
    Jeong, H., Oh, Y., Park, J.H., Koo, B.S., Lee, S.W.: Vision-based adaptive and recursive tracking of unpaved roads. Pattern Recognition Letters 23(13), 73–82 (2002)MATHCrossRefGoogle Scholar
  5. 5.
    Kluge, K., Thorpe, C.: The YARF system for vision-based road following. Mathematical and Computer Modelling 22(47), 213–233 (1995)MATHCrossRefGoogle Scholar
  6. 6.
    Lieb, D., Lookingbill, A., Thrun, S.: Adaptive road following using selfsupervised learning and reverse optical flow. In: Proc. Robotics Science and Systems, Cambridge, MA, USA, June 8-11 (2005)Google Scholar
  7. 7.
    Paetzold, F., Franke, U.: Road recognition in urban environment. Image and Vision Computing 18(5), 377–387 (2000)CrossRefGoogle Scholar
  8. 8.
    Park, J.W., Lee, J.W., Jhang, K.Y.: A lane-curve detection based on an LCF. Pattern Recognition Letters 24(14), 2301–2313 (2003)CrossRefGoogle Scholar
  9. 9.
    Rasmussen, C.: Grouping dominant orientations for ill-structured road following. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, June-2-July, vol. 1, pp. I–470 – I–477 (2004)Google Scholar
  10. 10.
    Wang, Y., Teoh, E.K., Shen, D.: Lane detection and tracking using b-snake. Image and Vision Computing 22(4), 269–280 (2004)CrossRefGoogle Scholar
  11. 11.
    Woodland, A., Labrosse, F.: On the separation of luminance from colour in images. In: Proceedings of the International Conference on Vision, Video and Graphics, Edinburgh, UK (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marek Ososinski
    • 1
  • Frédéric Labrosse
    • 1
  1. 1.Department of Computer ScienceAberystwtyh UniversityAberystwythUK

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