Abstract
We present a statistical method to detect regions whose apparent motion in the image is not conforming to the dominant motion of the background resulting from the camera movement. Alternatively, the same scheme can be used to track a particular region of interest of the scene. The apparent motion induced by the camera motion is represented by a 2D parametric motion model, and compensated for using the values of the motion model parameters estimated by a multiresolution robust statistical technique. Then, regions whose motion cannot be described by this global model estimated over the entire image, are extracted. The detection of these non conforming regions is achieved through a statistical regularization approach based on multiscale Markov random field (MRF) models. We have paid a particular attention to the definition of the energy function involved and to the observations taken into account. To gain robustness, information is integrated over time. This method has been validated by experiments carried out on many real image sequences.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
T. Aach, A. Kaup, and R. Mester, “Statistical model-based change detection in moving video,” Signal Processing, Vol. 31, pp. 165–180, 1993.
S. Ayer, H. S. Sawhney, and M. Gorkani, “Model-based 2d and 3d dominant motion estimation for mosaicing and video representation,” In Proc. IEEE Int. Conf. Comp. Vis., pp. 583-590, Boston, MA, June 1995.
R. Azencott, “Image analysis and Markov fields,” In ICIAM87: First Int. Conf. on Indust. and Appl. Math., pp. 53-61, Philadelphia, PA, June 1987.
J.L. Barron, D.J. Fleet, and S.S. Beauchemin, “Performance of Optical Flow techniques,” Int. Jour, of Comp. Vis., 12(1), pp. 43–77, Jan. 1994.
M. Bichsel, “Segmenting simply connected moving objects in a static scene,” IEEE Trans. Patt. Anal. Mach. Intell., Vol. 16(11), pp. 1138–1142, Nov. 1994.
P. Bouthemy and P. Lalande, “Recovery of moving object masks in an image sequence using local spatiotemporal contextual information,” Optical Eng., Vol. 32(6), pp. 1205–1212, June 1993.
B. Chalmond, “Image restoration using an estimated Markov model,” Sign. Proc., Vol. 15(2), pp. 115–129, Sept. 1988.
P.B. Chou and C.M. Brown, “The theory and practice of Bayesian image modeling,” Int. Jour. of Comp. Vis., Vol. 4, pp. 185–210, 1990.
S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images,” IEEE Trans. Patt. Anal. Mach. Intell., Vol. 6(6), pp. 721–741, Nov. 1984.
B. Girod, “Eyes movements and coding of video sequences,” In Proc. SPIE (Vis. Comm. and Im. Proc.) 88, Vol. 1001, pp. 398–405, 1988.
B.K.P. Horn and B.G. Schunck, “Determining optical flow,” Artif. Intell., Vol. 17, pp. 185–203, 1981.
P.J. Hubert, Robust statistics. Wiley, 1981.
M. Irani, B. Rousso, and S. Peleg, “Detecting and tracking multiple moving objects using temporal integration,” In Proc. of 2nd ECCV-92, S.Margherita Ligure, Italy, pp. 282-287. Springer-Verlag, May 1992.
M. Irani, B. Rousso, and S. Peleg, “Computing occluding and transparent motion,” Int. J. Comp. Vis., Vol. 12(1), pp. 5–16, 1994.
K.-P. Karmann, A.v. Brandt, and R. Gerl, “Moving object segmentation based on adaptive reference images,” In Sig. Proc. V: Theo. and Appl. (Proc. Fifth Euro. Sig. Proc. Conf), pp. 951-954, Barcelona, Sept. 1990.
H. Li and R. Forchheimer, “Image sequence coding at very low bitrates: a review,” IEEE Trans. on Im. Proc., Vol. 3(5), pp. 589–609, Sept. 1994.
R.C. Nelson, “Qualitative detection of motion by a moving observer,” Int. Jour. of Comp. Vis., Vol. 7(1), pp. 33–46, 1991.
E. Nguyen, C. Labit, and J.-M. Odobez, “A ROI approach to hybrid image sequence coding,” In 1st Int. Conf. on Im. Proc., Vol. 3, pp. 245–249, Austin, TX, Nov. 1994.
H. Nicolas and C. Labit, “Global motion identification for image sequence analysis and coding,” In Proc. ICASSP, Vol. IV, pp. 2825–2828, Toronto, May 1991.
J.-M. Odobez, Estimation, détection et segmentation du mouvement: une approche robuste et markovienne. PhD thesis, IRISA, University of Rennes I, N° 1304, Dec. 1994.
J.-M. Odobez and P. Bouthemy, “Direct model-based image motion segmentation for dynamic scene analysis,” In Proc of 2nd Asian Conf. Comp. Vis., Singapore, Dec. 1995.
J.-M. Odobez and P. Bouthemy, “Robust multiresolution estimation of parametric motion models,” Jour. of Vis. Comm. and Im. Repr., Vol. 6(4), pp. 348–365, Dec. 1995.
P. Perez, F. Heitz, and P. Bouthemy, “Multiscale minimization of global energy functions in some visual recovery problems,” CVGIP: Image Understanding, Vol. 59(1), pp. 125–134, Jan. 1994.
R. Thoma and M. Bierling, “Motion compensating interpolation considering covered and uncovered background,” Sig. Proc: Im. Comm., Vol. 1(2), pp. 191–212, Oct. 1989.
W.B. Thompson and T.-G. Pong, “Detecting moving objects,” Int. Jour. of Comp. Vis., Vol. 4, pp. 39–57, 1990.
W.B. Thompson, P. Lechleider, and E.R. Stuck, “Detecting moving objects using the rigidity constraint,” IEEE Trans. on Patt. Anal. and Mach. Intell, Vol. 2(15), pp. 162–166, Feb. 1993.
P.H.S. Torr and D.W. Murray, “Statistical detection of independent movement from a moving camera,” Im. and Vis. Comp., Vol. 11(4), pp. 180–187, May 1993.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1997 Springer Science+Business Media New York
About this chapter
Cite this chapter
Odobez, J.M., Bouthemy, P. (1997). Separation of Moving Regions from Background in an Image Sequence Acquired with a Mobil Camera. In: Li, H.H., Sun, S., Derin, H. (eds) Video Data Compression for Multimedia Computing. The Springer International Series in Engineering and Computer Science, vol 378. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6239-9_8
Download citation
DOI: https://doi.org/10.1007/978-1-4615-6239-9_8
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7862-4
Online ISBN: 978-1-4615-6239-9
eBook Packages: Springer Book Archive