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Segmentation of moving objects by robust motion parameter estimation over multiple frames

  • S. Ayer
  • P. Schroeter
  • J. Bigün
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 801)

Abstract

A method for detecting and segmenting accurately moving objects in monocular image sequences is proposed. It consists of two modules, namely a motion estimation and a motion segmentation module. The motion estimation problem is formulated as a time varying motion parameter estimation over multiple frames. Robust regression techniques are used to estimate these parameters. The motion parameters for the different moving objects are obtained by successive estimations on regions for which the previously estimated motion parameters are not valid. The segmentation module combines all motion parameters and the gray level information in order to obtain the motion boundaries and to improve them by using time integration. Experimental results on real image sequences with static or moving camera in the presence of multiple moving objects are reported.

Keywords

Optical Flow Motion Estimation Motion Parameter Motion Estimation Algorithm Static Segmentation 
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.

References

  1. 1.
    G Adiv. Determining three-dimensional motion and structure from optical flow generated by several moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:384–401, July 1985.Google Scholar
  2. 2.
    S. Ayer and P. Schroeter. Hierarchical robust motion estimation for segmentation of moving objects. In Eigth IEEE Workshop on Image and Multidimensional Signal Processing, pages 122–123, Cannes, France, September 1993.Google Scholar
  3. 3.
    S. Ayer, P. Schroeter, and J. Bigün. Tracking based on hierarchical multiple motion estimation and robust regression. In Time-Varying Image Processing and Moving Object Recognition, 3, Florence, Italy, June 1993.Google Scholar
  4. 4.
    J.R. Bergen, P. Anandan, K.J. Hanna, and J. Hingorani. Hierarchical model-based motion estimation. In Second European Conference on Computer Vision, pages 237–252, Santa Margherita Ligure, Italy, May 1992.Google Scholar
  5. 5.
    M.J. Black. Combining intensity and motion for incremental segmentation and tracking over long image sequences. In Second European Conference on Computer Vision, pages 485–493, Santa Margherita Ligure, Italy, May 1992.Google Scholar
  6. 6.
    M.J. Black and P. Anandan. A framework for the robust estimation of optical flow. In Fourth International Conference on Computer Vision, pages 231–236, Berlin, Germany, May 1993.Google Scholar
  7. 7.
    T. Darrell and A. Pentland. Robust estimation of a multi-layered motion representation. In IEEE Workshop on Visual Motion, pages 173–178, Nassau Inn, Princeton, NJ, October 1991.Google Scholar
  8. 8.
    M.G. Hall, A.V. Oppenheim, and A.S. Willsky. Time-varying parametric modeling of speech. Signal Processing, 5:267–285, 1983.Google Scholar
  9. 9.
    M. Irani, B. Rousso, and S. Peleg. Detecting and tracking multiple moving objects using temporal integration. In Second European Conference on Computer Vision, pages 282–287, Santa Margherita Ligure, Italy, May 1992.Google Scholar
  10. 10.
    P. Meer, D. Mintz, A. Rosenfeld, and D.Y. Kim. Robust regression methods for computer vision: A review. International Journal of Computer Vision, 6(1):59–70, 1991.Google Scholar
  11. 11.
    A. Rognone, M. Campani, and A. Verri. Identifying multiple motions from optical flow. In Second European Conference on Computer Vision, pages 258–266, Santa Margherita Ligure, Italy, May 1992.Google Scholar
  12. 12.
    P.J. Rousseeuw and A.M. Leroy. Robust Regression and Outlier Detection. John Wiley and Sons, New York, 1987.Google Scholar
  13. 13.
    P. Schroeter and J. Bigün. Image segmentation by multidimensional clustering and boundary refinement with oriented filters. In Gretsi Fourteenth symposium, pages 663–666, Juan les Pins, France, Septembre 1993.Google Scholar
  14. 14.
    S.K. Sethi and R. Jain. Finding trajectories of feature points in a monocular image sequence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9:56–73, January 1987.Google Scholar
  15. 15.
    W.B Thompson. Combining motion and contrast for segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2:543–549, 1980.Google Scholar
  16. 16.
    W.B. Thompson, P. Lechleider, and E.R. Stuck. Detecting moving objects using the rigidity constraint. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15:162–166, February 1993.Google Scholar
  17. 17.
    R. Wilson and M. Spann. Image Segmentation and Uncertainty. Research Studies Press Ltd., Letchworth, England, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • S. Ayer
    • 1
  • P. Schroeter
    • 1
  • J. Bigün
    • 1
  1. 1.Signal Processing LaboratorySwiss Federal Institute of TechnologyLausanneSwitzerland

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