Abstract
Video stabilization smooths camera motion estimates in a way that should adapt to different types of intentional motion. Corrective motion (the difference between smoothed and original motions) should be constrained so that black borders do not intrude into the (cropped) stabilized frames. Although offline smoothing can use all of the frames, online (real-time) smoothing can only use a small number of previous frames. In this paper, we propose an online motion smoothing method based on linear estimation applied to a constant-velocity model. We use estimate projection to ensure that the smoothed motion satisfies black-border constraints, which are modeled exactly by linear inequalities for general 2D motion models. We then combine the estimate projection with multiple-model estimation, which can adaptively smooth the camera motion in a probabilistic way. Experimental results show how the proposed algorithm can better smooth the camera motion and stabilize videos in real time.
Highlights
Video data has increased dramatically in recent years due to the prevalence of hand-held cameras
We propose an online motion smoothing method
6 Conclusions In this paper, we propose an online motion smoothing method for video stabilization based on the existing constant-velocity Kalman-filtering method
Summary
Video data has increased dramatically in recent years due to the prevalence of hand-held cameras. Compared to previous adaptive Kalman-filtering-based motion smoothing algorithms, the proposed dynamic multiple-model estimation is able to choose the proper parameters optimally from a probabilistic viewpoint. The Kalman filter estimate of the target locations from the CV model usually appears much smoother compared to the original noisy location measurements due to the constant-velocity assumption in the dynamic model This model has been successfully used in causal smoothing of time series such as the camera motion. The camera motion of the video can be parameterized as a sequence of motion vectors {θ k} This sequence can be smoothed via the CV-model-based Kalman filtering by setting the state vector as [ θ k, θk]T, where θk is the discrete changing rate (velocity) of the camera motion
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
