Advertisement

Efficient Computation of Optical Flow Using the Census Transform

  • Fridtjof Stein
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3175)

Abstract

This paper presents an approach for the estimation of visual motion over an image sequence in real-time. A new algorithm is proposed which solves the correspondence problem between two images in a very efficient way. The method uses the Census Transform as the representation of small image patches. These primitives are matched using a table based indexing scheme. We demonstrate the robustness of this technique on real-world image sequences of a road scenario captured from a vehicle based on-board camera. We focus on the computation of the optical flow. Our method runs in real-time on general purpose platforms and handles large displacements.

Keywords

Optical Flow Discriminative Power Signature Vector Road Scenario Consecutive Image Frame 
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.
    Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. International Journal of Computer Vision 12(1), 43–47 (1994)CrossRefGoogle Scholar
  2. 2.
    Cedras, C., Shah, M.: Motion-based recognition: A survey. IVC 13(2), 129–155 (1995)Google Scholar
  3. 3.
    Arribas, P.C., Macia, F.M.H.: FPGA Implementation of Camus Correlation Optical Flow Algorithm for real-time ImagesGoogle Scholar
  4. 4.
    Cutler, R., Turk, M.: View-based interpretation of real-time optical flow for gesture recognition. In: Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan (April 1998)Google Scholar
  5. 5.
    Camus, T.A., Bülthoff, H.H.: Real-time optical flow extended in time. Tech. Rep. 13, Tübingen, Germany (February 1995)Google Scholar
  6. 6.
    Enkelmann, W., Gengenbach, V., Krüger, W., Rössle, S., Tölle, W.: Hindernisdetektion durch Real-Zeit-Auswertung von optischen Fluß-Vektoren. In: Levi, P., Bräunl, T. (eds.) Autonome Mobile Systeme, pp. 285–295. Springer, Heidelberg (1994)Google Scholar
  7. 7.
    Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Proceedings of the Third European Conference on Computer Vision, Stockholm (May 1994)Google Scholar
  8. 8.
    Bhat, D., Nayar, S.: Ordinal measures for visual correspondence, pp. 351–357 (1996)Google Scholar
  9. 9.
    Beis, J.S., Lowe, D.G.: Indexing without invariants in 3d object recognition. PAMI 21(10), 1000–1015 (1999)Google Scholar
  10. 10.
    Veenman, C.J., Reinders, M.J.T., Backer, E.: Establishing motion correspondence using extended temporal scope, vol. 145(1-2), pp. 227–243 (April 2003)Google Scholar
  11. 11.
    Trucco, E., Verri, A.: Introductory Techniques for 3-D Computer Vision. Prentice-Hall, Englewood Cliffs (1998)Google Scholar
  12. 12.
    Woodfill, J., Von Herzen, B.: Real-time stereo vision on the parts reconfigurable computer. In: Proceedings IEEE Symposium on Field-Programmable Custom Computing Machines, Napa (April 1997)Google Scholar
  13. 13.
    Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition, Seattle, pp. 592–600 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Fridtjof Stein
    • 1
  1. 1.DaimlerChrysler AG, Research and TechnologyStuttgartGermany

Personalised recommendations