Long-term global motion estimation and its application for sprite coding, content description, and segmentation

We present a new technique for long-term global motion estimation of image objects. The estimated motion parameters describe the continuous and time-consistent motion over the whole sequence relatively to a fixed reference coordinate system. The proposed method is suitable for the estimation of affine motion parameters as well as for higher order motion models like the parabolic model-combining the advantages of feature matching and optical flow techniques. A hierarchical strategy is applied for the estimation, first translation, affine motion, and finally higher order motion parameters, which is robust and computationally efficient. A closed-loop prediction scheme is applied to avoid the problem of error accumulation in long-term motion estimation. The presented results indicate that the proposed technique is a very accurate and robust approach for long-term global motion estimation, which can be used for applications such as MPEG-4 sprite coding or MPEG-7 motion description. We also show that the efficiency of global motion estimation can be significantly increased if a higher order motion model is applied, and we present a new sprite coding scheme for on-line applications. We further demonstrate that the proposed estimator serves as a powerful tool for segmentation of video sequences.