A background modeling method for videos based on weighted statistical classification

In the field of intelligent video surveillance, foreground detection, moving target tracking and target recognition are the key technologies. They play an important role in target behavior analysis and understanding. In this paper a background modeling method based on weighted statistical classification is proposed. As a non-parametric background model, it uses several state categories to express multiple states of a background pixel. It does not require the background pixels to obey Gaussian distribution and needs no training. The weights are updated according to the matching history of the background pixel. The background state is determined by a threshold. Experiment results show that it obtains excellent detection results and real-time detection speed in complex scenes.