In many digital video applications, video sequences suffer from jerky movements between successive frames. In this paper, an integrated general-purpose stabilization method is proposed, which extracts the information from successive frames and removes the translation and rotation motions that result in undesirable effects. The scheme proposed starts with computation of the optical flow between consecutive video frames and an affine motion model is adopted in conjunction with the optical flow field obtained to estimate objects or cameramotions using the Horn-Schunck algorithm. The estimatedmotion vectors are then used by a model fitting filter to stabilize and smooth video sequences. Experimental results demonstrate that the proposed scheme is efficient due to its simplicity and provides good visual quality in terms of the global transformation fidelity measured by the peak-signal-noise-ratio.