Abstract
Aerial surveillance system provides a large amount of data compared with traditional surveillance system. But, it usually suffers from undesired motion of cameras, which presents new challenges. These challenges must be overcome before such video can be widely used. In this paper, we present a novel video stabilization and moving object detection system based on camera motion estimation. We use local feature extraction and matching to estimate global motion and we demonstrate that Scale Invariant Feature Transform (SIFT) keypoints are suitable for the stabilization task. After estimating the global camera motion parameters using affine transformation, we detect moving object by Kalman filtering. For motion smoothing, we use a median filter to retain the desired motion. Finally, motion compensation is carried out to obtain a stabilized video sequence. A number of aerial video examples demonstrate the effectiveness of our proposed system. We use the software Virtual Dub with the Deshaker-Plugin for test purposes. For objective evaluation, we use Interframe Transformation Fidelity for video stabilization tasks and Detection Ratio for moving object detection task.