BMVC 2004, Kingston, 7th-9th Sept, 2004

A Local Approach for Robust Optical Flow Estimation under Varying Illumination
Y.H. Kim (Purdue University, USA), A. M. Martinez (The Ohio State
University, USA) and A.C. Kak (Purdue University, USA)

The problem of motion estimation, in general, is made difficult by large illumination
variations and by motion discontinuities. In recent papers, we
and others have proposed global approaches to deal with both problems simultaneously
within the regularization framework. A major drawback of
such global methods is that several regularization parameters responsible for
the integration of the illumination and motion components need to be determined
in advance. This has reduced the applicability of global methods. In
this paper, a parameter-free local approach, which solves a linear regression
problem using a simple parametric model, is presented. To achieve robustness
for the linear regression problem, we introduce a modified version of the
least median of squares algorithm. We show quantitative error comparisons
between the results obtained by our local approach and those produced by
several global methods. Our results show that our local method is comparable
to the best results obtained by the global approaches yet does not require
any manual selection of parameters.
(pdf article)