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Statistical Change Detection by the Pool Adjacent Violators Algorithm
September 2011 (vol. 33 no. 9)
pp. 1894-1910
Alessandro Lanza, University of Bologna, Bologna
Luigi Di Stefano, University of Bologna, Bologna
In this paper, we present a statistical change detection approach aimed at being robust with respect to the main disturbance factors acting in real-world applications such as illumination changes, camera gain and exposure variations, noise. We rely on modeling the effects of disturbance factors on images as locally order-preserving transformations of pixel intensities plus additive noise. This allows us to identify within the space of all of the possible image change patterns the subspace corresponding to disturbance factors effects. Hence, scene changes can be detected by a-contrario testing the hypothesis that the measured pattern is due to disturbance factors, that is, by computing a distance between the pattern and the subspace. By assuming additive Gaussian noise, the distance can be computed within a maximum likelihood nonparametric isotonic regression framework. In particular, the projection of the pattern onto the subspace is computed by an O(N) iterative procedure known as Pool Adjacent Violators algorithm.

[1] R.J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, "Image Change Detection Algorithms: A Systematic Survey," IEEE Trans. Image Processing, vol. 14, no. 3, pp. 294-307, Mar. 2005.
[2] C.R. Wren, A. Azarbayejani, T. Darrell, and A.P. Pentland, "Pfinder: Real-Time Tracking of the Human Body," IEEE Trans. Pattern Analysis Machine Intelligence, vol. 19, no. 7, pp. 780-785, July 1997.
[3] C. Stauffer and W.E.L. Grimson, "Adaptive Background Mixture Models for Real-Time Tracking," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 246-252, June 1999.
[4] A. Elgammal, D. Harwood, and L. Davis, "Non-Parametric Model for Background Subtraction," Proc. IEEE Int'l Conf. Computer Vision, Sept. 1999.
[5] T. Aoki, O. Nakayama, M. Shiohara, and Y. Murakami, "Method and Apparatus for Detecting Moving Object," US Patent 6 931 146, Aug. 2005.
[6] K. Skifstad and R. Jain, "Illumination Independent Change Detection for Real World Image Sequences," J. Computer Vision, Graphics, and Image Processing, vol. 46, no. 3, pp. 387-399, June 1989.
[7] E. Durucan and T. Ebrahimi, "Change Detection and Background Extraction by Linear Algebra," Proc. IEEE, vol. 89, no. 10, pp. 1368-1381, Oct. 2001.
[8] R. Mester, T. Aach, and L. Dumbgen, "Illumination-Invariant Change Detection Using a Statistical Colinearity Criterion," Proc. 23rd DAGM Symp. Pattern Recognition, pp. 170-177, Sept. 2001.
[9] D. Toth, T. Aach, and V. Metzler, "Illumination-Invariant Change Detection," Proc. Fourth IEEE Southwest Symp. Image Analysis and Interpretation, pp. 3-7, Apr. 2000.
[10] J. Lou, H. Yang, W. Hu, and T. Tan, "An Illumination-Invariant Change Detection Algorithm," Proc. Fifth Asian Conf. Computer Vision, Jan. 2002.
[11] N. Ohta, "A Statistical Approach to Background Subtraction for Surveillance Systems," Proc. IEEE Int'l Conf. Computer Vision, vol. 2, pp. 481-486, July 2001.
[12] R. Zabih and J. Woodfill, "Non-Parametric Local Transforms for Computing Visual Correspondence," Proc. European Conf. Computer Vision, vol. 2, pp. 151-158, Sept. 1994.
[13] B. Froba and A. Ernst, "Face Detection with the Modified Census Transform," Proc. IEEE Int'l Conf. Automatic Face and Gesture Recognition, vol. 2, pp. 91-96, May 2004.
[14] D.N. Bhat and S.K. Nayar, "Ordinal Measures for Image Correspondence," IEEE Trans. Pattern Analysis Machine Intelligence, vol. 20, no. 4, pp. 415-423, Apr. 1998.
[15] A. Mittal and V. Ramesh, "An Intensity-Augmented Ordinal Measure for Visual Correspondence," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 849-856, June 2006.
[16] B. Xie, V. Ramesh, and T. Boult, "Sudden Illumination Change Detection Using Order Consistency," Image and Vision Computing, vol. 22, no. 2, pp. 117-125, Feb. 2004.
[17] R.E. Barlow, D.J. Bartholomew, J.M. Bremner, and H.D. Brunk, Statistical Inference under Order Restrictions. Wiley, 1972.
[18] T. Robertson, F.T. Wright, and L.R. Dykstra, Order Restricted Statistical Inference. Wiley, 1988.
[19] M. Ayer, H.D. Brunk, G.M. Ewing, W.T. Reid, and E. Silverman, "An Empirical Distribution Function for Sampling with Incomplete Information," Annals of Math. Statistics, vol. 26, no. 4, pp. 641-647, 1955.
[20] J.B. Kruskal, "Nonmetric Multidimensional Scaling: A Numerical Method," Psychometrika, vol. 29, no. 2, pp. 115-129, June 1964.
[21] A. Lanza and L.D. Stefano, "Detecting Changes in Gray Level Sequences by ML Isotonic Regression," Proc. Int'l Conf. Advanced Video and Signal-Based Surveillance, pp. 1-4, Nov. 2006.
[22] A. Lanza, "Detecting Changes in Video Sequences," PhD dissertation, Univ. of Bologna, Mar. 2007.
[23] A. Bevilacqua, L. Di Stefano, and A. Lanza, "A Simple Self-Calibration Method to Infer a Non-Parametric Model of the Imaging System Noise," Proc. IEEE Workshop Motion and Video Computing, 2005.
[24] B. Phong, "Illumination for Computer-Generated Images," Comm. ACM, vol. 18, no. 6, pp. 311-317, 1975.
[25] MUSCLE Network of Excellence. Motion Detection Video Sequences. Pattern Recognition and Image Processing Group, Vienna Univ. of Technology, http:/muscle.prip.tuwien.ac.at/, 2011.
[26] T. Fawcett, "An Introduction to ROC Analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861-874, June 2006.
[27] A.P. Bradley, "The Use of the Area under the ROC Curve in the Evaluation of Machine Learning Algorithms," Pattern Recognition, vol. 30, no. 7, pp. 1145-1159, July 1997.
[28] S. Mann, "Comparametric Equations with Practical Applications in Quantigraphic Image Processing," IEEE Trans. Image Processing, vol. 9, no. 8, pp. 1389-1406, Aug. 2000.
[29] F. Crow, "Summed-Area Tables for Texture Mapping," Computer Graphics, vol. 18, no. 3, pp. 207-212, 1984.
[30] A. Bevilacqua, L. Di Stefano, A. Lanza, and G. Capelli, "A Novel Approach to Change Detection Based on a Coarse-to-Fine Strategy," Proc. IEEE Int'l Conf. Image Processing, vol. 2, pp. 434-437, Sept. 2005.

Index Terms:
Change detection, motion detection, illumination invariance, isotonic regression, Pool Adjacent Violators Algorithm.
Citation:
Alessandro Lanza, Luigi Di Stefano, "Statistical Change Detection by the Pool Adjacent Violators Algorithm," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 9, pp. 1894-1910, Sept. 2011, doi:10.1109/TPAMI.2011.42
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