Direct Estimation of Long-term Global Motion Parameters Using Affine and Higher Order Polynomial Models

In this paper we present a new technique for the 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. It combines the advantages of feature matching and optical flow techniques. A hierarchical strategy is applied for the estimation, first translation, then 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. We also show that the efficiency of global motion estimation can be significantly increased if a higher-order motion model is applied.