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 about the usage of these methods in real-time applications. We concentrate in the speed of the algorithm and present a method that is fast enough to be used in video surveillance systems. We started from the ideas presented by Gaussian distribution for background generation. Instead of using actively all the pixels in the image 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 also the inactive pixels are classified as a part of the background or foreground We also reduce the frame frequency and use only every n(th) frame in the image sequence to construct adaptive background. This article is organised as follows: In Chapter 1 some of the previous work and their results are introduced. In Chapter 2 we first describe the method used by Stauffer and Grimson [3] and then present our new ideas. The results are explained in Chapter 3 and finally a conclusion is given.