Robust local optical flow estimation using bilinear equations for sparse motion estimation

This article presents a theoretical framework to decrease the computation effort of the Robust Local Optical Flow method which is based on the Lucas Kanade method. We show mathematically, how to transform the iterative scheme of the feature tracker into a system of bilinear equations and thus estimate the motion vectors directly by analyzing its zeros. Furthermore, we show that it is possible to parallelise our approach efficiently on a GPU, thus, outperforming the current OpenCV-OpenCL implementation of the pyramidal Lucas Kanade method in terms of runtime and accuracy. Finally, an evaluation is given for the Middlebury Optical Flow and the KITTI datasets.