How to Estimate Global Motion Non-Iteratively From a Coarsely Sampled Motion Vector Field

Nowadays, as the amount of video content increases, the importance of global motion estimation is increasing more and more. This paper considers motion estimation within a compressed domain and presents a robust non-iterative global motion estimation algorithm that efficiently eliminates outliers. The said algorithm is comprised of two parts. The first part uses the distinct-outlier mask based on statistical analysis to eliminate the distinct outliers and uses the current-object mask based on the reference-object mask to eliminate the object outliers. In the second part, the proposed median error designed to be robust to the outlier ratio has been used to estimate weights with higher values as the motion vector gets closer to global motion. Using these weights, the proposed algorithm can accurately estimate global motion non-iteratively, although the outlier ratio may change. The simulation results demonstrate that the proposed algorithm demonstrates the highest estimation accuracy and processing speed in synthetic motion fields as well as real video sequences.