Abstract
This paper deals with the motion detection problem. This issue is of key importance in many application fields. To solve this problem, we compute the dominant motion in the sequence using a wavelet analysis and robust techniques. So, we obtain an estimation of the dominant motion on several image resolutions. This method permits to define a hierarchical Markov model in a natural way. Thanks to this modelization, we overcome two problems: the solution sensibility in relation to the initial condition with a Markov random field, and the temporal aliasing. Moreover, we obtain a semi-iterative algorithm faster than using the multi-scale techniques. Thus, we introduce a fast and robust algorithm in order to compute the motion detection in an image sequence. This method is validated on 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
Aach, T., Duembgen, L., Mester, R., Toth, D.: Bayesian illumination-invariant motion detection. In: ICIP 2001, pp. 640–643 (2001)
Bernard, C.: Discrete Wavelet Analysis: A New Framework for Fast Optic Flow Computation. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 354–368. Springer, Heidelberg (1998)
Besag, J.: On the statistical analysis of dirty pictures. RoyalStat B-48(3), 259–302 (1986)
Bouthemy, P., Lalande, P.: Detection and tracking of moving objects based on a statistical regularization method in space and time. In: Faugeras, O. (ed.) ECCV 1990. LNCS, vol. 427, pp. 307–311. Springer, Heidelberg (1990)
Chang, M.M., Tekalp, A.M., Sezan, M.I.: Simultaneous motion estimation and segmentation. IP 6(9), 1326–1333 (1997)
Cremers, D.: A variational framework for image segmentation combining motion estimation and shape regularization. In: CVPR, pp. 53–58 (2003)
Heitz, F., Perez, P., Bouthemy, P.: Multiscale minimization of global energy functions in some visual recovery problems. CVGIP 59(1), 125–134 (1994)
Holland, P.W., WelschHsu, R.E.: Robust regression using iteratively reweighted least-squares. Commun. Statist.-Theor. Meth. A6, 813–827 (1977)
Hsu, Y.Z., Nagel, H.H., Rekers, G.: New likelihood test methods for change detection in image sequences. CVGIP 26(1), 73–106 (1984)
Irani, M., Rousso, B., Peleg, S.: Detecting and tracking multiple moving objects using temporal integration. In: Sandini, G. (ed.) ECCV 1992. LNCS, vol. 588, pp. 282–287. Springer, Heidelberg (1992)
Luthon, F., Caplier, A., Lievin, M.: Spatiotemporal mrf approach to video segmentation: Application to motion detection and lip segmentation. Sp 76(1), 61–80 (1999)
Mallat, S.G.: A theory for multiresolution signal decomposition: The wavelet representation. PAMI 11(7), 674–693 (1989)
Nguyen, H.T., Worring, M., Dev, A.: Detection of moving objects in video using a robust motion similarity measure. IEEE Trans. on Image Processing 9(1), 137–141 (2000)
Odobez, J.M., Bouthemy, P.: Detection of multiple moving objects using multiscale markov random fields, with camera compensation. In: ICIP 1994, pp. 257–261 (1994)
Paragios, N., Perez, P., Tziritas, G., Labit, C., Bouthemy, P.: Adaptative detection of moving objects using multiscale techniques. In: Proc. 3rd IEEE Int. Conf. Image Processing, ICIP 1996, Lausanne, Switzerland (September 1996)
Perez, P., Chardin, A., Laferte, J.M.: Noniterative manipulation of discrete energy-based models for image analysis. PR 33(4), 573–586 (2000)
Rosin, P.L.: Thresholding for change detection. CVIU 86(2), 79–95 (2002)
Sifakis, E., Tziritas, G.: Moving object localisation using a multi-label fast marching algorithm. Signal Processing: Image Communication 16(10), 963–976 (2001)
Thompson, W.B., Pong, T.C.: Detecting moving objects. IJCV 4(1), 39–58 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Demonceaux, C., Kachi-Akkouche, D. (2006). Motion Detection Using Wavelet Analysis and Hierarchical Markov Models. In: MacLean, W.J. (eds) Spatial Coherence for Visual Motion Analysis. SCVMA 2004. Lecture Notes in Computer Science, vol 3667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11676959_6
Download citation
DOI: https://doi.org/10.1007/11676959_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32533-8
Online ISBN: 978-3-540-32534-5
eBook Packages: Computer ScienceComputer Science (R0)