Background estimation with Gaussian distribution for image segmentation, a fast approach

Adaptive background updating is one of the methods used to detect moving objects in video sequences. Many techniques have been presented in this field, but there are few mentions of the use of these methods in real-time applications. We concentrate on the speed of the algorithm and present a method that is fast enough to be used in video surveillance systems. We started from ideas presented using Gaussian distribution for background generation. Instead of using all the pixels in the image actively, we divide the pixels into active and inactive ones. Gaussian distributions are used to model the history of active pixels and to state whether they belong to background or foreground. According to the classification of the previous active pixel, the inactive pixels are also classified as a part of the background or foreground. We also reduce the frame frequency and use only every nth frame in the image sequence to construct the adaptive background. Some of the previous work and their results are first introduced. We then describe the method used by C. Stauffer and W.E.L. Grimson (see IEEE Conf. on Computer Vision & Pattern Recognition, p.246-52, 1999) and present our new ideas. The results are explained.