Models of Statistical Visual Motion Estimation

Abstract

Several models of statistical estimation of motion from visual input are derived and analyzed theoretically and experimentally. We study a wide variety of models, ones that use least squares and ones that use maximum likelihood, with several different assumptions (dependent and independent noise, isotropic and non-isotropic noise), spherical and planar image surfaces, and different preprocessing (one based on correspondence and one based on disparity). We do all this analysis using only a few fundamental concepts from statistical estimation, so the relative merits and shortcomings of all the methods become evident. The experimental results provide a quantitative measure of these merits.