Maximizing uniform translational motion: Motion estimation with the Haar transform and dynamic programming

This paper proposes a new motion estimation framework based on localized linear transforms, multiresolutional probability models and dynamic programming. We incorporate localized linear transforms (specifically wavelets) into motion estimation by parameterizing the motion fields to be estimated in terms of their localized linear transform coefficients. In terms of these coefficients, we propose a simple multiresolutional probability model that captures the possible local smoothness in the field to be estimated while allowing for discontinuities and uncovered regions. Within this framework we formulate the motion estimation problem as a maximum likelihood optimization problem that can be tackled with dynamic programming to yield the globally optimal dense motion field.