Video shot from camera attached to moving devices like smartphone, and drone are often shaken because unwanted movements of the image sensors, which are caused by unstable motions of the devices during their operation (e.g. moving, fly). This phenomenon impacts on accuracy as well as effectiveness of systems that use camera videos as input data such as security surveillance and object tracking. In this paper, we propose a novel software-based system to stabilize camera videos in real-time by combining several general models introduced in existing studies. The main contribution of proposed system is the capability of processing instantaneously video achieved from moving devices to meet quality requirements by using Harris with Optical-flow, and Lucas-Kanade methods for motion estimation. We also propose several mechanisms including frame partition and matching for corner detector when applying Harris method to ensure processing quality and system performance. In our system, we also use Kalman filter for prediction model of motion compensation. Our experiments are carried out with videos having resolution of 640x480 pixels. Gained results proved that the average processing speed of our system can reach 35 fps, which satisfies the real-time requirement.
Keyword
causal system, motion prediction, performance, real-time, video stabilization
