Advertisement

Determining Feature Points for Classification of Vehicles

  • Wieslaw Pamula
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)

Abstract

The paper presents a discussion of feature point detectors characteristcs for use in road traffic vehicle classifiers. Prerequisities for classifying vehicles in road traffic scenes are formulated. Detectors requiring the least computational resources suitable for hardware implementation are investigated. A novel rank based morphological detector is proposed which provides good performance in corner detection which is of paramount importance in calculating projective invariants of moving objects.

Keywords

Feature Point Cellular Automaton Morphological Operation Corner Detection Geometric Invariant 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Autoscope Terra, Data sheet. Image Sensing Systems USA (2010)Google Scholar
  2. 2.
    Traficam vehicle presence sensor. Traficon N.V. (2010)Google Scholar
  3. 3.
    Bretherton, R.D., Bodger, M., Baber, N.: SCOOT - Managing Congestion Communications and Control. In: Proceedings of ITS World Congress, San Francisco, pp. 678–701 (2005)Google Scholar
  4. 4.
    Marivoet, S., De Moor, B.: Cellular automata models of road traffic. Physics Reports 419, 1–64 (2005)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Song, S.B., Lee, K.M., Lee, S.U.: Model based object recognition using geometric invariants of points and lines. Computer Vision and Image Understanding 84, 361–383 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Zhu, Y., Senevirante, L.D., Earles, S.W.E.: New algorithm for calculating an invariant od 3D point sets from single view. Image and Vision Computing 14, 179–188 (1996)CrossRefGoogle Scholar
  7. 7.
    Smith, S.M., Brady, J.M.: SUSANâĂŤa new approach to low level image processing. Int. J. Comput. Vis. 23, 45–78 (1997)CrossRefGoogle Scholar
  8. 8.
    Rosten, E., Drummond, T.: Machine Learning for High-Speed Corner Detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Burns, J.B., Weiss, R.S., Riseman, E.M.: View variation of point-set and line-segment features. IEEE Trans. Pattern Anal. Mach. Intell. 15, 51–68 (1993)CrossRefGoogle Scholar
  10. 10.
    Naegel, B., Passat, N., Ronse, B.: Grey-level hit-or-miss transforms Part I: Unified theory. Pattern Recognition 40, 635–647 (2007)CrossRefzbMATHGoogle Scholar
  11. 11.
    Soille, P.: On morphological operators based on rank filters. Pattern Recognition 35, 527–535 (2002)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Wieslaw Pamula
    • 1
  1. 1.Faculty of TransportSilesian University of TechnologyKatowicePoland

Personalised recommendations