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.
This work has been supported by Thomson-CSF, Rennes,France
Chapter PDF
Keywords
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
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.
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.
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.
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.
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.
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.
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.
M.G. Hall, A.V. Oppenheim, and A.S. Willsky. Time-varying parametric modeling of speech. Signal Processing, 5:267–285, 1983.
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.
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.
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.
P.J. Rousseeuw and A.M. Leroy. Robust Regression and Outlier Detection. John Wiley and Sons, New York, 1987.
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.
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.
W.B Thompson. Combining motion and contrast for segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2:543–549, 1980.
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.
R. Wilson and M. Spann. Image Segmentation and Uncertainty. Research Studies Press Ltd., Letchworth, England, 1988.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ayer, S., Schroeter, P., Bigün, J. (1994). Segmentation of moving objects by robust motion parameter estimation over multiple frames. In: Eklundh, JO. (eds) Computer Vision — ECCV '94. ECCV 1994. Lecture Notes in Computer Science, vol 801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028364
Download citation
DOI: https://doi.org/10.1007/BFb0028364
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-57957-1
Online ISBN: 978-3-540-48400-4
eBook Packages: Springer Book Archive