SEMI-ONLINE VIDEO STABILIZATION USING PROBABILISTIC KEYFRAME UPDATE AND INTER-KEYFRAME MOTION SMOOTHING

In this paper, we propose a video stabilization method that takes advantages of both online and offline video stabiliza-tion methods in a semi-online framework. Our approach takes the fixed length incoming frames for having the advantages of offline methods with the same length of delayed results. We stabilize input frames by warping to the keyframe to in-crease the visual stability. For preserving user intent camera motion correctly, we determine the next keyframe update by measuring inconsistency between a current keyframe and in-coming frames. Moreover, inter-frame motion smoothing by quadratic fitting bridges the keyframes smoothly for pleasant viewing experiences. Our algorithm not only handles rapid camera motion changes, but also stabilizes the input camera path smoothly in real-time. Experimental results show that the proposed algorithm is comparable to the state-of-the-art offline video stabilization methods with only fixed length of incoming frames