  Video stabilization technique is essential for most hand-held captured videos
due to high-frequency shakes. Several 2D-, 2.5D- and 3D-based stabilization
techniques are well studied, but to our knowledge, no solutions based on deep
neural networks had been proposed. The reason for this is mostly the shortage
of training data, as well as the challenge of modeling the problem using neural
networks. In this paper, we solve the video stabilization problem using a
convolutional neural network (ConvNet). Instead of dealing with offline
holistic camera path smoothing based on feature matching, we focus on
low-latency real-time camera path smoothing without explicitly representing the
camera path. Our network, called StabNet, learns a transformation for each
input unsteady frame progressively along the time-line, while creating a more
stable latent camera path. To train the network, we create a dataset of
synchronized steady/unsteady video pairs via a well designed hand-held
hardware. Experimental results shows that the proposed online method (without
using future frames) performs comparatively to traditional offline video
stabilization methods, while running about 30 times faster. Further, the
proposed StabNet is able to handle night-time and blurry videos, where existing
methods fail in robust feature matching.
