An efficient background updating model for motion detection

In transit system, military, residential area and restricted area video surveillance system is getting more popular for motion detection and object extraction. Background updating process is the most important feature for motion detection in video surveillance system. In this paper, we proposed a novel algorithm for updating the background and therefore detection the motion of an object for a fixed video surveillance system. In our proposed method video frames are taken from a surveillance camera and then for updating background previous 40 frames from the video frames are used. Here pixel wise comparison is done for the previous frames so that maximum common pixel values are stored to get a temporary background. When the temporary background is obtained then it is compared to the last frame's pixel values and the common pixel values are stored as the permanent background. The pixel values which are not common to the both frame then these values are taken from the previous permanent frames which are common with the last frame. Now again if there is any mismatched pixel value remains then the temporary background's pixel value is stored as permanent background for this position. Finally we get the permanent background as an updated background. And now for motion detection, the next frame's pixel values are subtracted with the permanent background, if the value goes beyond a threshold value then there the object motion is detected. We have applied this method for different video datasets and obtained interesting and promising results.