Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames

Optical flow research has made significant progress in recent years and it can now be computed efficiently and accurately for many images. However, complex motions, large displacements, and difficult imaging conditions are still problematic. In this paper, we present a framework for estimating optical flow which leads to improvements on these difficult cases by 1) estimating occlusions and 2) using additional temporal information. First, we divide the image into discrete triangles and show how this allows for occluded regions to be naturally estimated and directly incorporated into the optimization algorithm. We additionally propose a novel method of dealing with temporal information in image sequences by using “inertial estimates” of the flow. These estimates are combined using a classifier-based fusion scheme, which significantly improves results. These contributions are evaluated on three different optical flow datasets, and we achieve state-of-the-art results on MPI-Sintel.