Implementation of a Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications

Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient. We propose an approach based on self organization through artificial neural networks, widely applied in human image processing systems and more generally in cognitive science. The proposed approach can handle scenes containing moving backgrounds, gradual illumination variations and camouflage, has no bootstrapping limitations and achieves robust detection for different types of videos taken with stationary cameras. Index Terms Background subtraction, Motion detection, Neural Networks, Competitive Learning, self organization, Visual surveillance.