Locally Adapted Gain Control for Reliable Foreground Detection

Abstract

One of the first steps in video analysis systems is the detection of objects moving in the scene, namely the foreground detection. Therefore, the accuracy and precision obtained in this phase have a strong impact on the performance of the whole system. Many camera manufacturers include internal systems, such as the automatic gain control (AGC), so as to improve the image quality; although some of these options enhance the human perception, they may also introduce sudden changes in the intensity of the overall image, which risk to be wrongly interpreted as moving objects by traditional foreground detection algorithms. In this paper we propose a method able to detect the changes introduced by the AGC, and properly manage them, so as to minimize their impact on the foreground detection algorithms. The experimentation has been carried out over a wide and publicly available dataset by adopted one well known background subtraction technique and the obtained results confirm the effectiveness of the proposed approach.