Nonparametric on-line background generation for surveillance video

A novel two-stage background generation method is proposed. In the first stage, an intensity-level based statistical approach is employed to identify a variety of background variations. No background training is needed. In the second stage, a background variation based heuristic framework is designed to generate a synchronized background video sequence on-line from the surveillance video. This framework employs both static and dynamic spatial clues in the scene. The quantitative evaluations demonstrate the method can generate more realistic background images than some well-known background modeling techniques.