Two classes of algorithms for modeling camera motion in video sequences captured by a camera are proposed. The first class can be applied when there is no camera translation and the motion of the camera can be adequately modeled by zoom, pan, and rotation parameters. The second class is more general in that it can be applied when the camera is undergoing a translation motion, as well as a rotation and zoom and pan. This class uses seven parameters to describe the motion of the camera and requires the depth map to be known at the receiver. The salient feature of both algorithms is that the camera motion is estimated using binary matching of the edges in successive frames. The rate distortion characteristics of the algorithms are compared with that of the block matching algorithm and show that the former provide performance characteristics similar to those of the latter with reduced computational complexity.