Efficient adaptive density estimation per image pixel for the task of background subtraction

Bishop, 1995 Cemgil, A.T., Zajdel, W., Krose, B., 2005. A hybrid graphical model for robust feature extraction from video. In: Proc. of the Conf. on Computer Vision and Pattern Recognition. Egan, 1975 Elgammal, A., Harwood, D., Davis, LS., 2000. Non-parametric background model for background subtraction. In: Proc. of the European Conf. of Computer Vision. Everingham, M.R., Muller, H., Thomas, B.T., 2002. Evaluating image segmentation algorithms using the Pareto front. In: Proc. of the 7th European Conf. on Computer Vision. pp. 34–48. Friedman, N., Russell, S., 1997. Image segmentation in video sequences: a probabilistic approach. In: Proc. 13th Conf. on Uncertainty in Artificial Intelligence. Hall, 1995, Improved variable window kernel estimates of probability densities, Ann. Statist., 23, 1, 10.1214/aos/1176324451 Harville, M., 2002. A framework for high-level feedback to adaptive, per-pixel, mixture-of-Gaussian background models. In: Proc. of the European Conf. on Computer Vision. Hayman, E., Eklundh, J.-O., 2003. Statistical background subtraction for a mobile observer. In: Proc. of the Internat. Conf. on Computer Vision. pp. 67–74. KaewTraKulPong, P., Bowden, R., 2001. An improved adaptive background mixture model for real-time tracking with shadow detection. In: Proc. of 2nd European Workshop on Advanced Video Based Surveillance Systems. Kato, 2002, An HMM-based segmentation method for traffic monitoring movies, IEEE Trans. Pattern Anal. Mach. Intell., 24, 1291, 10.1109/TPAMI.2002.1033221 Lee, 2005, Effective Gaussian mixture learning for video background subtraction, IEEE Trans. Pattern Anal. Mach. Intell., 27, 827, 10.1109/TPAMI.2005.102 Mittal, A., Paragios, N., 2004. Motion-based background subtraction using adaptive kernel density estimation. In: Proc. of the Conf. on Computer Vision and Pattern Recognition. Monnet, A., Mittal, A., Paragios, N., Ramesh, V., 2003. Background modeling and subtraction of dynamic scenes. In: Proc. of the Internat. Conf. on Computer Vision. pp. 1305–1312. Pareto, V., 1971. Manual of political economy, A.M. Kelley, New York (Original in French 1906). Power, P.W., Schoonees, J.A., 2002. Understanding background mixture models for foreground segmentation. In: Proc. of the Image and Vision Computing New Zealand. Prati, 2003, Detecting moving shadows: Formulation, algorithms and evaluation, IEEE Trans. Pattern Anal. Mach. Intell., 25, 918, 10.1109/TPAMI.2003.1206520 Stauffer, C., Grimson, W., 1999. Adaptive background mixture models for real-time tracking. In: Proc. of the Conf. on Computer Vision and Pattern Recognition. pp. 246–252. Stenger, B., Ramesh, V., Paragios, N., Coetzec, F., Buhmann, J.M., 2001. Topology free hidden Markov models: application to background modeling. In: Proc. of the Internat. Conf. on Computer Vision. Toyama, K., Krumm, J., Brumitt, B., Meyers, B., 1999. Wallflower: principles and practice of background maintenance. In: Proc. of the Internat. Conf. on Computer Vision. Titterington, 1984, Recursive parameter estimation using incomplete data, J. Roy. Statist. Soc., Ser. B (Methodological), 2, 257 Wand, 1995 Wren, 1997, Pfinder: Real-time tracking of the human body, IEEE Trans. Pattern Anal. Mach. Intell., 19, 780, 10.1109/34.598236 Withagen, P.J., Schutte, K., Groen, F., 2002. Likelihood-based object tracking using color histograms and EM. In: Proc. of the Internat. Conf. on Image Processing. pp. 589–592. Zhang, 1996, A survey on evaluation methods for image segmentation, Pattern Recognition, 29, 1335, 10.1016/0031-3203(95)00169-7 Zivkovic, 2004, Recursive unsupervised learning of finite mixture models, IEEE Trans. Pattern Anal. Mach. Intell., 26, 651, 10.1109/TPAMI.2004.1273970