Video Stabilization

Video stabilization aims to remove unwanted camera shake and motion blur from videos, improving visual quality and enabling more robust downstream applications. Current research focuses on developing efficient and accurate algorithms, often employing deep learning models like recurrent neural networks and transformers, to estimate and compensate for camera motion, sometimes incorporating 3D multi-frame fusion or optical flow analysis. These advancements are significant for various fields, including robotics, remote collaboration, and video quality assessment, by enhancing the reliability and usability of video data in diverse applications.