Real-time adaptive background modeling in fast changing conditions

Background modeling in fast changing scenarios is a challenging task due to unexpected events like sudden illumination changes, reflections, and shadows, which can strongly affect the accuracy of the foreground detection. In this paper, we describe a real-time and effective background modeling approach, called FAFEX, that can deal with global and rapid changes in the scene background. The method is designed to identify variations in the background geometry of the monitored scene and it has been quantitatively tested on a publicly available data set, containing a varied set of highly dynamic environments. The experimental evaluation demonstrates how our method is able to effectively deals with challenging sequences in real-time.