Improving Mixture of Gaussians background model through adaptive learning and Spatio-Temporal voting

The Mixture of Gaussians (MoG) is a frequently used method for moving objects detection in a video, but its parameter setting is often tricky for dynamic scenes. Therefore, in this paper we propose an adaptive algorithm for the parameters learning by using working set of recent random samples. Furthermore, a novel Spatio-Temporal voting scheme is introduced to refine the foreground map. Also, we propose a components shifting based technique for handling abrupt scene changes. The components shifting based scheme can reutilize most of the already learned models, thus avoids a large number of false alarms by quickly adapting to the changed illumination conditions. The proposed model is rigorously tested and compared with several state-of-the-art methods and has shown significant performance improvements.