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Automatic real-time road marking recognition using a feature driven approach

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Abstract

Automatic road marking recognition is a key problem within the domain of automotive vision that lends support to both autonomous urban driving and augmented driver assistance such as situationally aware navigation systems. Here we propose an approach to this problem based on the extraction of robust road marking features via a novel pipeline of inverse perspective mapping and multi-level binarisation. A trained classifier combined with additional rule-based post-processing then facilitates the real-time delivery of road marking information as required. The approach is shown to operate successfully over a range of lighting, weather and road surface conditions.

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References

  1. Bishop R.: Intelligent vehicle applications worldwide. Intell. Syst. Appl. IEEE 15(1), 78–81 (2000)

    Article  Google Scholar 

  2. Campbell N.W., Pout M.R., Priestly M.D.J., Dagless E.L., Thomas B.T.: Autonomous road vehicle navigation. Eng. Appl. Artif. Intell. 7(2), 177–190 (1994)

    Article  Google Scholar 

  3. Maurer, M.. Dickmanns, E.D.: A system architecture for autonomous visual road vehicle guidance. ITSC 97. IEEE Conference on Intelligent Transportation System, pp. 578–583 (1997)

  4. Hoffmann, G.M., Tomlin, C.J., Montemerlo, F., Thrun, S.: Autonomous Automobile Trajectory Tracking for Off-Road Driving: Controller Design, Experimental Validation and Racing. American Control Conference, 2007 ACC’07, pp. 2296–2301 (2007)

  5. Broggi, A.: Robust real-time lane and road detection in critical shadow conditions. In: Proceedings of International Symposium on Computer Vision, pp. 353–358 (1995)

  6. Charbonnier, P., Diebolt, F., Guillard, Y., Peyret, F.: Road markings recognition using image processing. In: IEEE Conference on Intelligent Transportation System, ITSC 97, pp. 912–917 (1997)

  7. Rebut J., Bensrhair A., Toulminet G.: Image segmentation and pattern recognition for road marking analysis. IEEE Int. Symp. Ind. Electron. 1(4–7), 727–732 (2004)

    Google Scholar 

  8. Barnard S.T.: Interpreting perspective images. Artif. Intell. 21, 435–462 (1983)

    Article  Google Scholar 

  9. Magee M.J., Aggmal J.K.: Determining vanishing points from perspective images. Comput. Vis. Graphics Image Processing 26, 256–267 (1984)

    Article  Google Scholar 

  10. Quan L., Mohr R.: Determining perspective structures using hierarchical Hough transform. Pattern Recognit. Lett. 9(44), 279–286 (1989) Elsevier

    Article  MATH  Google Scholar 

  11. Lutton E., Maitre H., Lopez-Krahe J.: Contribution to the determination of vanishing points using Hough transform. Pattern Anal. Mach. Intell. 16(4), 430–438 (1994)

    Article  Google Scholar 

  12. Cantoni, V., Lombardi, L., Porta, M., Sicard, N.: Vanishing point detection: representation analysis and new approaches. In: Proceedings of 11th International Conference on Image Analysis and Processing Volume, Issue, pp. 90–94 (2001)

  13. Almansa A., Desolneux A., Vamech S.: Vanishing point detection without any a priori information. Pattern Anal. Mach. Intell. 25(4), 502–507 (2003)

    Article  Google Scholar 

  14. Nakatani, H., Kimura, S., Saito, O.: Extraction of vanishing point and its application to scene analysis based on image sequence. In: Proceedings of the 5th International Pattern Recognition Conference (1980)

  15. Matessi, A., Lombardi, L.: Vanishing point detection in the hough transform space. In: Proceedings of the Fifth International Euro-Par Conference, Tolouse, France, pp. 987–994 (1999)

  16. McLean G.f., Kotturi D.: Vanishing point detection by line clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(11), 1090–1095 (1995)

    Article  Google Scholar 

  17. Pratt W.K.: Digital Image Processing, 3rd edn. John, New York (2001)

    Book  Google Scholar 

  18. Wu, S.T., Marquez, M.R.G.: A non-self-intersection Douglas-Peucker algorithm. In: Proc. XVI Brazilian Symposium on Computer Graphics and Image Processing, pp. 60–66 (2003)

  19. Sebe N., Lew M.S.: Robust Computer Vision: Theory and Applications. Kluwer, Norwell (2003)

    MATH  Google Scholar 

  20. Trier Ø.D., Jain A.K., Taxt T.: Feature extraction methods for character recognition—a survey. Pattern Recognit. 29(4), 641–662 (1996)

    Article  Google Scholar 

  21. Bishop C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1996)

    MATH  Google Scholar 

  22. Kastrinaki V., Zervakis M., Kalaitzakis K.: A survey of video processing techniques for traffic applications. Image Vis. Comput. 21, 359–381 (2003)

    Article  Google Scholar 

  23. Forsyth D., Ponce J.: Computer Vision—A Modern Approach. Prentice-Hall, New Jersey (2003)

    Google Scholar 

  24. Bertozzi M., Broggi A., Fascioli A.: Stereo inverse perspective mapping: theory and applications. Image Vis. Comput. J. 8(16), 585–590 (1998)

    Article  Google Scholar 

  25. Hu M.-K.: Visual pattern recognition by moment invatiants. IRE. Tran. Inf. Theory 8, 179–187 (1962)

    MATH  Google Scholar 

  26. Bishop C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1996)

    MATH  Google Scholar 

  27. Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of the ICNN, San Francisco, pp. 586–591 (1993)

  28. Kundu A., He Y., Bahl P.: Recognition of hand-written word: first and second order hidden Markov model based approach. Pattern Recognit. 22(3), 283–297 (1989)

    Article  Google Scholar 

  29. Li, Y., He, K., Jia, P.: Road markers recognition based on shape information. In: Proc. IEEE Int. Symp. on Intelligent Vehicles Symposium, pp. 117–122 (2007)

  30. Noda, M., Takahashi, T., Deguchi, D., Ide, I., Murase, H., Kojima, Y., Naito, T.: Recognition of road markings from in-vehicle camera images by a generative learning method. In: Proc. IAPR Conf. on Machine Vision Applications, 15–5 (2009)

  31. Turk M., Pentland A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  32. Canny J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

    Article  Google Scholar 

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Authors and Affiliations

  1. Applied Mathematics and Computing Group, School of Engineering, Cranfield University, Bedfordshire, UK

    Alireza Kheyrollahi & Toby P. Breckon

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  1. Alireza Kheyrollahi
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  2. Toby P. Breckon
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Correspondence to Toby P. Breckon.

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Kheyrollahi, A., Breckon, T.P. Automatic real-time road marking recognition using a feature driven approach. Machine Vision and Applications 23, 123–133 (2012). https://doi.org/10.1007/s00138-010-0289-5

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