Real-time Video Stabilization and Mosaicking for Monitoring and Surveillance

Video stabilization and mosaicking is an important task when a video stream of a large area, such as the video stream from a UAV or a blimp, is received. In this paper we propose a novel approach for video stabilization and mosaicking from shaky video streams. We present a feature-based real-time video mosaicking pipeline performing image alignment by combining feature point detector, descriptor, a robust statistical selection methodology (RANSAC), and other filters. By using this method, we can eliminate the mechanical stabilizer. The stabilization is done using SURF feature detectors, BRSIK descriptor, and affine transition matrix. To reach real-time performance, we used C++ OpenCV libraries for implementation and compared most feature detectors and descriptors by their time consumption factor to find out which one is better for real-time approach. Finally, the approach has been tested on real video streams whic showed good performance.