In this paper, we propose an efficient, robust, and fast method for the estimation of global motion from image sequences. The method is generic in that it can accommodate various global motion models, from a simple translation to an eight-parameter perspective model. The algorithm is hierarchical and consists of three stages. In the first stage, a low-pass image pyramid is built. Then, an initial translation is estimated with full-pixel precision at the top of the pyramid using a modified n-step search matching. In the third stage, a gradient descent is executed at each level of the pyramid starting from the initial translation at the coarsest level. Due to the coarse initial estimation and the hierarchical implementation, the method is very fast. To increase robustness to outliers, we replace the usual formulation based on a quadratic error criterion with a truncated quadratic function. We have applied the algorithm to various test sequences within an MPEG-4 coding system. From the experimental results we conclude that global motion estimation provides significant performance gains for video material with camera zoom and/or pan. The gains result from a reduced prediction error and a more compact representation of motion. We also conclude that the robust error criterion can introduce additional performance gains without increasing computational complexity.