E.P.Ong, M.Spann
Robust Optical Flow
Computation based on
Least-Median-of-Squares
Regression.
Int. Journal of
Computer
Vision, Vol 31, No. 1, Feb 1998, pp51-82
Abstract
An optical flow estimation technique is presented which
is
based on the least-median-of-squares (LMedS)
robust regression
algorithm
enabling more accurate flow estimates to be computed in the vicinity of
motion discontinuities. The flow is computed in a blockwise fashion
using
an affine model. Through the use of overlapping blocks coupled with a
block
shifting strategy, redundancy is introduced into the computation of the
flow. This eliminates blocking effects common in most other techniques
based on blockwise processing and also allows flow to be accurately
computed
in regions containing three distinct motions.
A multiresolution version of the technique is also
presented,
again based on LMedS regression, which enables image sequences
containing
large motions to be effectively handled.
An extensive set of quantitative comparisons with a
wide
range of previously published methods are carried out using synthetic,
realistic (computer generated images of natural scenes with known flow)
and natural images. Both angular and absolute flow errors are
calculated
for those sequences with known optical flow. Displaced frame difference
error, used extensively in video compression, is used for those natural
scenes with unknown flow. In all of the sequences tested, a comparison
with those methods that result in a dense flow field (greater than 80%
spatial coverage), show that the LMedS technique produces the least
error
irrespective of the error measure used.
