Empirical choice of smoothing parameters in robust optical flow estimation

Optical flow estimation algorithms such as the Lukas-Kanade method and Horn and Schunk method require selection of a tuning parameter. In the former case, it is a neighbourhood size, in the latter, a penalty parameter. Selection of these tuning parameters is difficult in general but has a profound effect on the results. Therefore, automatic methods of selection are of great interest. In previous work, we developed selection methods for the above algorithms. Now we develop a selection procedure for a robust version of the Lukas-Kanade method. This is a non-trivial task since the robust algorithm is nonlinear.