Nonlocal Regularizing Constraints in Variational Optical Flow

Optical flow methods try to estimate a dense correspondence field describing the motion of the objects in an image sequence. We introduce novel nonlocal regularizing constraints for variational optical flow computation. While the use of similarity weights has been restricted to the regularization term so far, the proposed data terms permit to implicitly use the image geometry in order to regularize the flow and better locate motion discontinuities. The experimental results illustrate the superiority of the new constraints with respect to the classical brightness constancy assumption as well as to nonlocal regularization strategies.