IEEE Transactions on Signal Processing,
vol. 62, no. 13, pp. 3293-3304, Jul. 1, 2014.
Constrained 3D Rotation Smoothing via Global
Manifold Regression for Video Stabilization
Brian L. Evans
Department of Electrical
and Computer Engineering,
The University of Texas at Austin,
Austin, TX 78712 USA
Paper Draft -
Software Release -
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We present a novel motion smoothing algorithm for hand-held cameras with
application to video stabilization.
Video stabilization seeks to remove unwanted frame-to-frame jitter due to
For video stabilization, we use a pure 3D rotation motion model with
known camera projection parameters.
The 3D camera rotation can be reliably tracked by a gyroscope as commonly
found on a smart phone or tablet.
In this paper, we directly smooth the sequence of camera rotation matrices
for the video frames by exploiting the Riemannian geometry on a manifold.
Our contributions are
The geodesic-distance-based smoothness metric better exploits the manifold
structure of sequences of rotation matrices.
The geodesic-convex constraints effectively guarantee that no black borders
intrude into the stabilized frames.
The proposed manifold optimization algorithm can find the global optimal
solution in only a few iterations.
Experimental results show that video stabilization based on our motion
smoothing algorithm outperforms state-of-the-art methods by generating
videos with less jitter and without black borders.
- formulation of motion smoothing as a geodesic-convex constrained
regression problem on a non-linear manifold based on geodesic distance,
- computation of gradient and Hessian of the objective function using
Riemannian geometry for gradient-related manifold optimization, and
- generalization of the two-metric projection algorithm in Euclidean
space to manifolds to solve the proposed manifold optimization
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