Video stabilization based on GMS and warping transform

Abstract

Video stabilization is a video enhancement technology that improves the original video quality by eliminating unnecessary camera motion. In the last decade of research, video stabilization has changed from a simple solution aimed at computational simplicity to a complex solution aimed at stabilization effects. We propose a novel method based on Grid-based Motion Statistics(GMS) and warping transformation, stabilizing video with less cropping. Specifically, feature points are firstly matched by GMS, and RANSAC is applied within each frame to estimate the motion vectors accurately. Furthermore, we incorporate predicted adaptive path smoothing to produce stable trajectories and generate stable video with warping transformation. Moreover, to the best of our knowledge, the proposed algorithm has less cropping and better stability than previous work. The experimental results demonstrate the performance of our method on a large variety of consumer videos.