Video Stabilization for Fast Moving Camera Based on Feature Point Classification

This paper is dedicated to video stabilization for fast moving photographing based on feature points classification, especially for the moving camera in various speeds. The proposed method mainly consists of feature point detection and classification, calculating global motion vector and rotation angle of frame, and frame compensation. It is first to search feature points and then classify these feature points into foreground (i.e., moving object) type and background type based on multiple view geometry and DBSCAN algorithm. Then, the global feature points and their optical-flows are derived and utilized for calculating the global motion vector and global rotation angle of frame. Finally, both global motion vector and global rotation angle are refined through motion smoothing using a Kalman filter for providing better frame compensation to generate stable frames. Experimental results show that the proposed method can moderately stabilize the frames captured by a moving camera.