Fast feature-based video stabilization without accumulative global motion estimation

Abstract

This paper presents a novel digital video stabilization approach that provides both efficiency and robustness. In this approach, features of each frame are first detected by the oriented features from accelerated segment test (FAST) method, and then the corresponding features between consecutive frames are matched by a very fast binary descriptor which is based on the rotated binary robust independent elementary features (BRIEF). The oriented FAST combined with the rotated BRIEF, which is called ORB, is very efficient in feature detection and matching, and can be used to speed up the motion estimation without any hardware acceleration. In addition, an improved motion smoothing method is proposed to smooth affine model based motion parameters without accumulative global motion estimation. Unlike the conventional method, the proposed method uses unstable input frames and stabilized output frames instead of original input frames to estimate motion parameters directly, allowing for more desirable motion parameters. Experiments with a variety of videos demonstrate that the proposed approach is both efficient and robust.