Blue overlay is painted on first frame and carried by the flow
over subsequent frames.

Comment: this shows computing flow over a medium range of motions
*without drift*.
Many flow algorithms can only compute small motions,
and the strategy of registering each frame to the next and composing
the computed flow would result in noticable drift.

#### Benchmark Details:

The Barron/Fleet/Beauchemin error is the angle in degrees
between ground truth and computed vectors; each vector is
(u,v,1) normalized. This gives a measure of both
directional and distance error in a single number.
The best reported dense flow score in the Barrons et.al. survey (mid 90s)
was avgerr=11.26+std.dev=16.41 for a modified Horn-Schunck.
A more competitive result is that from M.Black's code: avgerr=8.6,std.dev=9.3.
The program described here scores avgerr=6.04+std.dev=7.71.

The Yosemite benchmark is CG rendered digital elevation data
for which the correct ground truth flow is known.
The sky, however, is a layered evolving Perlin noise thing
that does not have a single correct flow velocity.
Because of this errors on this benchmark can never be zero.
Cropping out the sky region results in a score of
avgerr=2.65+std.dev=1.87 for the current program.

One frame from Yosemite benchmark

Flow computed with discontinuity detection, average error 6.04.

Flow computed using M.Black algorithm for comparison,
average error 8.6.