We propose the use of a fast algorithm for estimating background models. This algorithm makes use of Parzen density estimation in non-stationary scenes. Each pixel has a probability density function this is used to approximate the value of pixels observed in a video sequence. Estimating this function quickly and accurately is important. In our approach, theprobability density function is partially updated within the range of a window function based on the value observed. The model quickly adapts to changes in the scene and foreground objects can be robustly detected. Several experiments show the effectiveness of our approach.