EURASIP Journal on Image and Video Processing,
Mar. 27, 2017, DOI: 10.1186/s13640-017-0171-8.
Online Motion Smoothing for Video Stabilization
via Constrained Multiple-model Estimation
Chao Jia (1)
Brian L. Evans (2)
(1) Qualcomm Inc., 92121 San Diego, CA, USA.
(2) Wireless Networking and
Department of Electrical
and Computer Engineering
The University of Texas at Austin,
Austin, TX 78712 USA
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.
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Last Updated 03/27/17.