Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Attention Mechanisms
A self-organizing approach to detection of moving patterns for real-time applications
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
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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.