Automatic, robust global motion estimation using clustering

Global motion estimation (GME) is a vital part of many video compression and computer vision applications. However, the large moving foreground objects that are present in many video scenes make the task of GME more challenging. In this paper, we propose an automatic, efficient, and robust approach for GME that addresses the issue of large foreground objects. The proposed GME algorithm is based on two key ideas: a new clustering technique, to automate the initial segmentation of background and foreground blocks, and a modified Lorentzian estimator, to reduce the impact of any remaining foreground blocks on the GME process. We also apply an up-sampling technique to the estimated motion parameters to remove any errors caused by under-sampling during the warping process. These ideas provide a significant improvement in performance when combined into a common framework. Simulation results and analyses demonstrate the improved performance of our proposed algorithm over other state-of-the-art methods.