Dealing with Gradual Lighting Changes in Video Surveillance for Indoor Environments

Background subtraction is a commonly used method to segment moving objects in image sequences. By comparing new frames to a background model, regions of interest can be found. To cope with highly dynamic and complex environments, a Mixture of Gaussian Models technique has been proposed and has gained tremendous popularity. This paper analyzes its performance in typical indoor environments, like subways, and shows the lack of this technique to cope with gradual illumination changes. Furthermore, computational expensiveness of the maintenance of the Gaussian Models is shown. Consequently, in this paper we propose the use of different updating and matching mechanisms to improve the general robustness and speed. Finally, experimental results are presented to show the gain of our proposed system, according to the standard technique.