Foreground background traffic scene modeling for object motion detection

Almost every computer vision applications used background subtraction method to detect moving objects from video sequence. Moving object detection and tracking is generally the first step in many applications such as face detection, traffic surveillance, object recognition, detection of unattended bags, people counting etc. Background modeling is very useful and effective method for locating objects of interest in videos. Since many methods existed in literature is based on the assumption that variation occur in image are only due to movement of object of interest (i.e. the scene background is remain stationary during whole period of video), but these method has limited application because when scene shows a continuous dynamic behavior, such an assumption is violated and object detection performance is deteriorates. Proposed method detects a moving object from a video and tracks them. This paper provides new strategies, which extract a pure background frame. With the help of this pure background frame for background subtraction, this technique obtained one binary foreground image. The other one is acquired with the help of frame differencing. Finally a moving object are detected using spatial correlation of moving objects in background subtracted frames, for the pixel present in binary image obtain by frame differencing. We confirmed that our proposed method is effective for real world video.