Research On Electronic Video Stabilization Algorithm For Rotational Video

With the development of information technology, unmanned aerial vehicles(UAV), surveillance projectile and other high-tech weapons have been the focus of attention of the world, and is widely used in a variety of tasks aerial reconnaissance, electronic jamming, target tracking and battlefield assessment. As the UAV and surveillance projectile flight characteristics and complexity of their environment which led to the derived videos are often in a large angle of rotation and shaky state, so that the observer is difficult to obtain accurate information, and even lead to false positives and false negatives. Therefore, to stabilize the video, eliminate video rotation and jitter, have extremely important meanings to improve UAV and surveillance projectile performance. This paper studies the principles of electronic video stabilization algorithm, analyzes the advantages and disadvantages of the main steps and puts forward the corresponding improvements, the main work is as follows:(1)The basic principles and key issues of electronic video stabilization is analyzed in detail. The imaging model under moving camera and inter-frame motion model is derived from the way of the camera movement. Then, introduce the basic principles and classical algorithms of motion estimation, motion filter and motion compensation, summarizes the advantages and disadvantages of different algorithms.(2)For the video derived from the camera in the presence of rotation, translation and zoom, the common video stabilization algorithm is difficult to obtain accurate motion vectors. The Fourier-Mellin algorithm based on SVD(Singular Value Decomposition) is proposed, the rank 1 approximation of the phase correlation matrix is obtained by SVD, the impact of aliasing effect is reduced, the precision of the estimation is improved, while the computational complexity is reduced. Then, the rotation is eliminated by rotating the video frame reversely using the obtained rotation angle. In the motion filter module, the optimization method is using to obtain the stable path of the camera, both the smoothness of the camera path and the fidelity of the video information are taken into target equation. Experiment results show that the algorithm can obtain the inter-frame motion vector precisely, the rotation of the video is eliminated effectively, the video jitter is suppressed while the loss of the video information is reduced.(3) For the video derived from the camera in the presence of rotation, translation, zoom, tilt and pan, the inter-frame motion vector is very complex. The SURF(speeded-up robust features) point-feature matching algorithm is employed to find the corresponding matching points between two consecutive frames. then, the bidirectional nearest neighbor distance ratio method is used to eliminate false matches; the high accuracy rotation angle of inter-frame is obtained using affine transformation model and RANSAC(random sample consensus) algorithm, and the video rotation is removed by rotating the current frame reversely. Secondly, the standard KLT(Kanade-Lucas-Tomasi) point-feature tracking algorithm is used to extract the point-feature trajectories from the video. Finally, the Kalman filtering and B-spline curve fitting are combined and adopted to smooth the point-feature trajectories. The stabilized video can be rendered using the new trajectories by full-frame warping. The experiment results confirm that the proposed approach is suitable for the video with any rotation angle, has a high rotation removing accuracy, can stabilize the video effectively, and also has a strong robustness for the video with low resolution and motion blur.