Reliability Analysis for Global Motion Estimation

Abstract

Global motion estimation (GME) is the enabling step for many important video exploitation tasks. In this work, we focus on indirect GME methods which have low computational complexity. Typically, an indirect GME method has two major steps. The first step is to find point correspondence between frames through local motion search or feature matching. Then, the second step determines global motion parameters using optimal model fitting, such as least mean-squared error (LMSE) fitting or RANSAC. However, due to image noise and inherent ambiguity in point correspondence, local motion estimation often suffers from relatively large errors, which degrade the performance and reliability of GME. In this work, we propose a method to characterize the reliability of local motion estimation results and use this reliability measure as a weighting factor to determine the importance level of each local motion estimation result during global motion estimation. Our simulation results demonstrate that the proposed scheme is able to significantly improve the accuracy and robustness of global motion estimation with a very small computational overhead.