  Video stabilization algorithms are of greater importance nowadays with the
prevalence of hand-held devices which unavoidably produce videos with
undesirable shaky motions. In this paper we propose a data-driven online video
stabilization method along with a paired dataset for deep learning. The network
processes each unsteady frame progressively in a multi-scale manner, from low
resolution to high resolution, and then outputs an affine transformation to
stabilize the frame. Different from conventional methods which require explicit
feature tracking or optical flow estimation, the underlying stabilization
process is learned implicitly from the training data, and the stabilization
process can be done online. Since there are limited public video stabilization
datasets available, we synthesized unstable videos with different extent of
shake that simulate real-life camera movement. Experiments show that our method
is able to outperform other stabilization methods in several unstable samples
while remaining comparable in general. Also, our method is tested on complex
contents and found robust enough to dampen these samples to some extent even it
was not explicitly trained in the contents.
