Evaluation of a feature-based global-motion estimation system

Global-motion estimators are an important part of current video-coding systems like MPEG-4, content analysis and description systems like MPEG-7, and many video-object segmentation algorithms. Feature-based motion estimators use the motion vectors obtained for a set of selected points to calculate the parameters of the globalmotion model. This involves the detection of feature points, the computation of correspondences between two sets of features, and the motion parameter estimation. In this paper, we will present a feature-based global-motion estimation system and discuss each of its parts in detail. The idea is to provide an overview of a general purpose feature-based motion estimator and to point out the important design aspects. We evaluate the performance of different feature detection algorithms, propose an efficient feature-correspondence algorithm, and we compare the difference between a non-linear parameter estimation and a linear approximation. Finally, the RANSAC based robust parameter estimation is examined, we show why it does not reach its theoretical performance, and propose a modification to increase its accuracy. Our global-motion estimator has an average accuracy of ˜ 0.15 pixels with real-time execution. Keywords: Global-motion estimation, feature-point detection, robust estimation, feature correspondences.