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

In this paper, we present a new technique for longterm 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.