Countering illumination variations in a video surveillance environment

In the field of video technology for surveillance applications it is often necessary to cope with the phenomenon of illumination variations. In fact, if not compensated, such variations can falsely trigger the change detection module that detects intrusions in video surveillance systems, thus affecting their reliability. Many studies have been made to solve the change detection problem under varying illumination conditions. Most of the published methods, however, rely only on the luminance information. The algorithm proposed in this paper exploits independently the information of each band of the RGB color space of the video sequences, thus producing a change detection algorithm that is more robust to illumination variations. These illumination variations are globally modeled by the so- called Von Kries model (also known as diagonal scaling model). This model is generally used to solve the color constancy problems, where conformance to a reference image illumination has to be guaranteed, like in color image retrieval applications. The use of this model is motivated by its low computational cost and by the interest of studying the relationship between color constancy and change detection. Based on practical experiments which confirm the interest in this method, new and more robust change detection algorithms are expected to be designed. In addition, the paper proposes the use of an iterative scheme whose aim is to improve the results obtained in the change detection module, and which is independent of this module, i.e., it can be used with other change detection schemes. It will be shown that the iteration can improve the quality of the final change mask, thus permitting to obtain a more effective change detection scheme.