Statistical model-based change detection in moving video

Abstract A major issue with change detection in video sequences is to guarantee robust detection results in the presence of noise. In this contribution, we first compare different test statistics in this respect. The distributions of these statistics for the null hypothesis are given, so that significance tests can be carried out. An objective comparison between the different statistics can thus be based on identical false alarm rates. However, it will also be pointed out that the global thresholding methods resulting from the significance approach exhibit certain weaknesses. Their shortcomings can be overcome by the Markov random field based refining method derived in the second part of this paper. This method serves three purposes: it accurately locates boundaries between changed and unchanged areas, it brings to bear a regularizing effect on these boundaries in order to smooth them, and it eliminates small regions if the original data permits this.

[1]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[2]  Walter L. Smith.  Probability and Statistics , 1959, Nature.

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

[4]  I. Reed,et al.  A Detection Algorithm for Optical Targets in Clutter , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[5]  A. Dale Magoun,et al.  Decision, estimation and classification , 1989 .

[6]  H. Joel Trussell,et al.  Comments on "Nonstationary Assumptions for Gaussian Models in Images" , 1978, IEEE Trans. Syst. Man Cybern..

[7]  H. Derin,et al.  Segmentation of textured images using Gibbs random fields , 1986 .

[8]  Robert THOMA,et al.  Motion compensating interpolation considering covered and uncovered background , 1989, Signal Process. Image Commun..

[9]  A. S. Elfishawy,et al.  Adaptive algorithms for change detection in image sequence , 1991, Signal Process..

[10]  J. Besag.  Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

[11]  Thomas F. Quatieri,et al.  Object detection by two-dimensional linear prediction , 1983, ICASSP.

[12]  R. F. W. Pease,et al.  Combining intraframe and frame-to-frame coding for television , 1974 .

[13]  Don R. Hush,et al.  Change detection for target detection and classification in video sequences , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[14]  Ciro Cafforio,et al.  Methods for measuring small displacements of television images , 1976, IEEE Trans. Inf. Theory.

[15]  T.F. Quatieri,et al.  Statistical model-based algorithms for image analysis , 1986, Proceedings of the IEEE.

[16]  L. Masera,et al.  Foreground/background segmentation in videotelephony , 1989, Signal Process. Image Commun..

[17]  R. Lenz,et al.  Image Sequence Coding Using Scene Analysis and Spatio-Temporal Interpolation , 1983 .

[18]  Rudolf Mester,et al.  Statistical Model Based Image Segmentation Using Region Growing, Contour Relaxation And Classification , 1988, Other Conferences.

[19]  B. G. Haskell,et al.  A frame-to-frame picturephone® coder for signals containing differential quantizing noise , 1973 .

[20]  Petros Maragos,et al.  Morphological filters-Part II: Their relations to median, order-statistic, and stack filters , 1987, IEEE Trans. Acoust. Speech Signal Process..

[21]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[22]  Denis J. Connor,et al.  Properties of Frame-Difference Signals Generated by Moving Images , 1974, IEEE Trans. Commun..

[23]  Patrick Bouthemy,et al.  Detection and Tracking of Moving Objects Based on a Statistical Regularization Method in Space and Time , 1990, European Conference on Computer Vision.

[24]  Johan Wiklund,et al.  Image Sequence Analysis for Object Tracking. , 1987 .

[25]  Hans-Hellmut Nagel,et al.  New likelihood test methods for change detection in image sequences , 1984, Comput. Vis. Graph. Image Process..

[26]  Tomaso A. Poggio.  Early vision: From computational structure to algorithms and parallel hardware , 1985, Comput. Vis. Graph. Image Process..

[27]  Til Aach,et al.  Segmentation of Image Pairs and Sequences by Contour Relaxation , 1988, DAGM-Symposium.

[28]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Patrick Bouthemy,et al.  Motion detection in an image sequence using Gibbs distributions , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[30]  Band , 1943 .

[31]  Norbert Diehl,et al.  Object-oriented motion estimation and segmentation in image sequences , 1991, Signal Process. Image Commun..