Time Dependent On-line Boosting for Robust Background Modeling

In modern video surveillance systems change and outlier detection is of highest interest. Most of these systems are based on standard pixel-by-pixel background modeling approaches. In this paper, we propose a novel robust blockbased background model that is suitable for outlier detection using an extension to on-line boosting for feature selection. In order to be robust our system incorporates several novelties for previous proposed on-line boosting algorithms and classifier-based background modeling systems. We introduce time-dependency and control for on-line boosting. Our system allows for automatically adjusting its temporal behavior to the underlying scene by using a control system which regulates the model parameters. The benefits of our approach are illustrated on several experiments on challenging standard datasets.