A Self-organizing Neural System for Background and Foreground Modeling
In this paper we propose a system that is able to detect moving objects in digital image sequences taken from stationary cameras and to distinguish wether they have eventually stopped in the scene. Our approach is based on self organization through artificial neural networks to construct a model of the scene background that can handle scenes containing moving backgrounds or gradual illumination variations, and models of stopped foreground layers that help in distinguishing between moving and stopped foreground regions, leading to an initial segmentation of scene objects. Experimental results are presented for color video sequences that represent typical situations critical for video surveillance systems.
Keywordsbackground modeling foreground modeling neural network self organization visual surveillance
Unable to display preview. Download preview PDF.
- 1.Cantoni, V., Marinaro, M., Petrosino, A. (eds.): Visual Attention Mechanisms. Kluwer Academic/Plenum Publishers, New York (2002)Google Scholar
- 2.Collins, R.T., Lipton, A.J., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., Hasegawa, O., Burt, P., Wixson, L.: A System for Video Surveillance and Monitoring, The Robotics Institute, Carnegie Mellon University, CMU-RI-TR-00-12 (2000)Google Scholar
- 6.Maddalena, L., Petrosino, A.: A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications. IEEE Trans. Image Process (July 2008) (to be published)Google Scholar
- 10.Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and Practice of Background Maintenance. In: Proc. of the Seventh IEEE Conference on Computer Vision, vol. 1, pp. 255–261 (1999)Google Scholar
- 11.Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-Time Tracking of the Human Body. IEEE Trans. on PAMI 19(7), 780–785 (1997)Google Scholar