Abstract: The traditional background models based on pixels can not interpret the background motion efficiently although fast in computation. Optical flow can represent object motion accurately, but can not meet the requirements of real time application for computational complexity. In this literature, the traditional background models based on pixels and optical flow are fused with the purpose of combining their advantages, which are used to formulate a novel two model background modeling approach for detecting moving objects fast in computation and accurate in detection. The traditional background models based on pixels are used to model static backgrounds using statistics of pixel intensity, while statistics on intensity, spatial and temporal information of pixels are extracted to generate the optical flow field, which is utilized to model moving ones. Then we can use the two models for moving objects detection fast and accurately. The advantage is that the intensity background model can discriminate foreground from static background fast and accurately, so global optical flow field is not necessary and computational complexity is reduced; the optical flow background model for moving backgrounds can represent background motion very well, mitigate noise caused by background motion remarkably and detect moving objects accurately and then is superior to the previous two methods. This two model-based background modeling strategy can reduce the noise generated by background motion significantly and detect moving objects fast and robustly, as illustrated in our experiments.