Background Suppression for Visual Surveillance Using SOM

Abstract

Background suppression is widely used approach for detection of moving objects in video streams which 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. An approach based on self-organization through artificial neural networks, widely applied in human image processing systems and more generally in cognitive science is proposed. This approach can handle scenes containing moving backgrounds, gradual illumination variations and camouflage, has no bootstrapping limitations, can include into the background model shadows cast by moving objects, and achieves robust detection for different types of videos taken with stationary cameras.

Keywords: background suppression, motion detection, neural network, self-organization, visual surveillance

Cite this Article

Thomas SP, Jose A, ResmaChandran VP, Thomas SA. Background Suppression for Visual Surveillance Using SOM. Journal of Communication Engineering & Systems (JoCES). 2015; 5(1): 15–20p.