The accurate estimation of motion in image sequences is
of central importance to numerous computer vision applications.
Most competitive algorithms compute flow fields
by minimizing an energy made of a data and a regularity
term. To date, the best performing methods rely on rather
simple purely geometric regularizers favoring smooth motion.
In this paper, we revisit regularization and show that
appropriate adaptive regularization substantially improves
the accuracy of estimated motion fields. In particular, we
systematically evaluate regularizers which adaptively favor
rigid body motion (if supported by the image data) and motion
field discontinuities that coincide with discontinuities
of the image structure. The proposed algorithm relies on sequential
convex optimization, is real-time capable and outperforms
all previously published algorithms by more than
one average rank on the Middlebury optic flow benchmark.