Reliable detection of camera motion based on weighted optical flow fitting

Our goal in this paper is the reliable detection of camera motion (pan/zoom/tilt) in video records. We propose an algorithm based on weighted optical flow least-square fitting, where an iterative procedure is used to improve the corresponding weights. To the optical flow computation we used the Kanade-Lucas-Tomasi feature tracker. Besides detecting camera motion, our algorithm provides a precise and reliable quantitative analysis of the movements. It also provides a rough segmentation of each frame into "foreground" and "background" regions, corresponding to the moving and stationary parts of the scene, respectively. Tests with two real videos show that the algorithm is fast and efficient, even in the presence of large objects movements.