Stitching Stabilizer: Two-frame-stitching Video Stabilization for Embedded Systems
This addresses the need for efficient video stabilization on resource-constrained devices like smartphones, though it is incremental as it builds on prior multi-frame fusion methods.
The paper tackles the problem of high computational cost in video stabilization for embedded systems by proposing a two-frame stitching algorithm that achieves real-time processing for Full HD videos at 30 FPS, producing more stabilized and natural results than existing solutions from Microsoft and Instagram.
In conventional electronic video stabilization, the stabilized frame is obtained by cropping the input frame to cancel camera shake. While a small cropping size results in strong stabilization, it does not provide us satisfactory results from the viewpoint of image quality, because it narrows the angle of view. By fusing several frames, we can effectively expand the area of input frames, and achieve strong stabilization even with a large cropping size. Several methods for doing so have been studied. However, their computational costs are too high for embedded systems such as smartphones. We propose a simple, yet surprisingly effective algorithm, called the stitching stabilizer. It stitches only two frames together with a minimal computational cost. It can achieve real-time processes in embedded systems, for Full HD and 30 FPS videos. To clearly show the effect, we apply it to hyperlapse. Using several clips, we show it produces more strongly stabilized and natural results than the existing solutions from Microsoft and Instagram.