Motion Intent Analysis-Based Full-Frame Video Stabilization

Abstract

Video stabilization aims to eliminate random jitter in video sequences, but most methods result in stabilized video with degraded resolution and content loss. In this letter, we propose a full-frame video stabilization algorithm based on motion intent analysis. The algorithm consists of three main steps: motion estimation, motion smoothing, and video completion. First, robust keypoints are extracted using the improved SuperPoint network and refined with the suppression via square covering (SSC) algorithm to obtain stable and reliable keypoints. Then, the Lucas-Kanade algorithm is applied for motion estimation of inter-frame matched feature points. Second, motion smoothing is achieved using the Kalman filtering algorithm to remove the high-frequency jitter component from the trajectory, and motion compensation is applied to the original video sequence to generate a stable image sequence. Finally, to preserve the original video resolution, we propose a video completion method based on motion intent analysis. Experimental results demonstrate that our method achieves higher stability while maintaining the original video resolution compared to the current state-of-the-art video stabilization algorithms.