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This paper presents a new robust global motion estimation method based on pre-analysis of the video content. The novel idea in the proposed method, compared to classical robust statistics-based estimation methods, is to classify the video sequences into 3 classes based on the analysis of scene content before motion estimation. Different motion models and estimation methods are applied to different classes of image sequences. As a result, outliers can be identified and removed from the dominant motion estimate to solve the problem of inaccurate initial descending direction estimates associated with classical global motion estimation methods. The pre-analysis of scene content is based on the Spatial Temporal Gradient Scale (STGS) images derived from the original image sequences. The extra computation time for STGS-image-based pre-analysis of scene content is negligible compared to the overall speed and accuracy improvement achieved with the proposed method. Evaluations based on extensive experiments have shown that the proposed method significantly improves the speed of robust global motion estimation methods (saving about 50% of the execution time of classical methods).