A Temporal Regularizer for Large Optical Flow Estimation

The aim of this work is to propose a model for computing the optical flow in a sequence of images with a spatio-temporal regularizer explicitly designed for large displacements. We study the introduction of a temporal regularizer that expands the information beyond two consecutive frames. We use the large optical flow constraint equation in the data term, the Nagel-Enkelmann operator for the spatial smoothness term and a newly designed temporal regularization. Our model is based on an energy functional that yields a partial differential equation (PDE). This PDE is embedded into a multipyramidal strategy to recover large displacements. The numerical experiments show that thanks to this regularizer the results are more stable and accurate.