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.