Symmetrical Dense Optical Flow Estimation with Occlusions Detection

Traditional techniques of dense optical flow estimation don’t generally yield symmetrical solutions: the results will differ if they are applied between images I₁ and I₂ or between images I₂ and I₁. In this work, we present a method to recover a dense optical flow field map from two images, while explicitely taking into account the symmetry across the images as well as possible occlusions and discontinuities in the flow field. The idea is to consider both displacements vectors from I₁ to I₂ and I₂ to I₁ and to minimise an energy functional that explicitely encodes all those properties. This variational problem is then solved using the gradient flow defined by the Euler-Lagrange equations associated to the energy. In order to reduce the risk to be trapped within some irrelevant minimum, a focusing strategy based on a multi-resolution technique is used to converge toward the solution. Promising experimental results on both synthetic and real images are presented to illustrate the capabilities of this symmetrical variational approach to recover accurate optical flow.