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Vehicles Recognition Using Fuzzy Descriptors of Image Segments

  • Bartłomiej Płaczek
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 57)

Summary

In this paper a vision-based vehicles recognition method is presented. Proposed method uses fuzzy description of image segments for automatic recognition of vehicles recorded in image data. The description takes into account selected geometrical properties and shape coefficients determined for segments of reference image (vehicle model). The proposed method was implemented using reasoning system with fuzzy rules. A vehicles recognition algorithm was developed based on the fuzzy rules describing shape and arrangement of the image segments that correspond to visible parts of a vehicle. An extension of the algorithm with set of fuzzy rules defined for different reference images (and various vehicle shapes) enables vehicles classification in traffic scenes. The devised method is suitable for application in video sensors for road traffic control and surveillance systems.

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References

  1. 1.
    Bezdek, J., Keller, J., Krisnapuram, R., Pal, N.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Springer, New York (2005)Google Scholar
  2. 2.
    Płaczek, B., Staniek, M.: Model Based Vehicle Extraction and Tracking for Road Traffic Control. In: Kurzyñski, M., et al. (eds.) Advances in Soft Computing. Computer Recognition Systems, vol. 2, Springer, Heidelberg (2007)Google Scholar
  3. 3.
    Dongjin, H., Leotta, M., Cooper, D., Mundy, J.: Vehicle class recognition from video-based on 3D curve probes. In: 2nd Joint IEEE Int. Workshop on Visual Surveillance, pp. 285–292 (2005)Google Scholar
  4. 4.
    Ferryman, J., Worrall, A., Sullivan, G., Baker, K.: A Generic Deformable Model for Vehicle Recognition. In: BMVC, Birmingham, vol. 1, pp. 127–136 (1995)Google Scholar
  5. 5.
    Gupte, S., Masoud, O., Martin, R., Papanikolopoulos, N.: Detection and Classification of Vehicles. IEEE Trans. on ITS 3(1), 37–47 (2002)Google Scholar
  6. 6.
    Haag, M., Nagel, H.: Tracking of complex driving manoeuvres in traffic image sequences. Image and Vision Computing 16, 517–527 (1998)Google Scholar
  7. 7.
    Hongliang, B., Jianping, W., Changpin, L.: Motion and haar-like features based vehicle detection. In: 12th Int. IEEE Conf. Multi-Media Modelling, pp. 356–359 (2006)Google Scholar
  8. 8.
    Mohottala, S., Kagesawa, M., Ikeuchi, K.: Vehicle Class Recognition Using 3D CG. In: Proc. of 2003 ITS World Congress (2003)Google Scholar
  9. 9.
    Płaczek, B., Staniek, M.: Fuzzy rules for model based vehicles classification. In: Central European Conf. on Inf. and Int. Systems, Varazdin, pp. 493–500 (2008)Google Scholar
  10. 10.
    Xiaoxu, M., Grimson, W.: Edge-based rich representation for vehicle classification. In: Tenth IEEE Int. Conf. on Computer Vision, vol. 2, pp. 1185–1192 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Bartłomiej Płaczek
    • 1
  1. 1.Faculty of TransportSilesian University of TechnologyKatowicePoland

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