Optical Flow Estimation using Phase Only-Correlation

In this work, we present an optical flow algorithm with a highly reduced set of motion vector candidates that are obtained by the Phase-Only Correlation function. For each candidate and each pixel, we estimate the cost of assigning the candidate vector to that pixel using a sophisticated filter that is robust to edges, instead of the most commonly used box filter for block matching. The proposed filter is based on the minimization of a quadratic energy function using the Gauss-Seidel optimization method. Preliminary results with images from the Middlebury database show that the proposed method reduces the angular and end-point errors by up to 25% with respect to the classic block matching method.