Abstract: To improve the video's image quality of airborne imaging systems, we proposed a real-time image stabilization system, based on fast smoothing point-feature trajectories. In this paper, a system framework was established to eliminate the accumulative errors between frames in global motion estimation and achieve fast and long-time video stabilization. This framework was different from the traditional real-time video stabilization. Firstly, an improved SURF algorithm was introduced to extract unstable feature points from original shaky video. Secondly, we determined the adjacency of these feature points, generated point feature trajectories with Delaunay triangulation algorithm and smoothed them with Kalman filtering. Then, we could estimate the global motion vectors which directly needed to be compensated from the original point-feature trajectories and the smoothing point-feature trajectories. The experiment results indicate that the proposed method can be used to stabilize unstable aerial video in real time. Also, it can effectively improve the image quality of the airborne imaging systems and estimate global motion vectors between independent frames. So it can be used in long-time image stabilization.