Adaptive detection of moving objects using multiscale techniques

In this paper we address an important issue in motion analysis: the detection of moving objects. A statistical approach is adopted in order to formulate the problem. The inter-frame difference is modeled by a mixture of Laplacian distributions, and a Gibbs random field is used for describing the label set. A new method to determine the regularization parameter is proposed, based on a voting technique. Then two different multiscale algorithms are evaluated, and the labeling problem is solved using either ICM (iterated conditional modes) or HCF (highest confidence first) algorithms. Experimental results are provided using synthetic and real video sequences.

[1]  Martin Bichsel,et al.  Segmenting Simply Connected Moving Objects in a Static Scene , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  M. Bertero,et al.  Ill-posed problems in early vision , 1988, Proc. IEEE.

[3]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[4]  Nikolas P. Galatsanos,et al.  Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation , 1992, IEEE Trans. Image Process..

[5]  Til Aach,et al.  Bayesian algorithms for adaptive change detection in image sequences using Markov random fields , 1995, Signal Process. Image Commun..

[6]  Jean-Marc Odobez,et al.  Robust Multiresolution Estimation of Parametric Motion Models , 1995, J. Vis. Commun. Image Represent..

[7]  P. Pérez,et al.  Multiscale minimization of global energy functions in some visual recovery problems , 1994 .

[8]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[9]  Bernard W. Silverman,et al.  A Fast and Efficient Cross-Validation Method for Smoothing Parameter Choice in Spline Regression , 1984 .

[10]  Georgios Tziritas,et al.  Detection and location of moving objects using deterministic relaxation algorithms , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[11]  Haluk Derin,et al.  Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  David Malah,et al.  Change detection and texture analysis for image sequence coding , 1994, Signal Process. Image Commun..

[13]  X. Guyon,et al.  On the choice of the regularization parameter: the case of binary images in the Bayesian restoration framework , 1991 .