A fast video stabilization system based on speeded-up robust features

Abstract

A fast and efficient video stabilization method based on speeded-up robust features (SURF) is presented in this paper. The SURF features are extracted and tracked in each frame and then refined through Random Sample Consensus (RANSAC) to estimate the affine motion parameters. The intentional camera motions are filtered out through Adaptive Motion Vector Integration (AMVI). Experiments performed on several video streams illustrate superior performance of the SURF based video stabilization in terms of accuracy and speed when compared with the Scale Invariant Feature Transform (SIFT) based stabilization method. © 2011 Springer-Verlag.