Non-negative sparse coding for motion extraction

Abstract

Visual motion is a rich source of information that is directly coupled to the underlying shape of a moving object. One way to describe motion is to use optical flow fields. Due to the aperture problem, dense optical flow estimation is an ill-constraint problem, while sparse optical flow estimation looses the shape information of moving objects. Current estimation algorithms based on regularization or segmentation fail at surface deformations or when the relevant motion is less dominant then its sourrounding movements. Both is e.g. true for face movements, where small movement patterns, so called action units, need to be preserved for further image analysis. We present a novel approach to capture the characteristics of local motion patterns that is based on the brightness constancy equation of optical flow estimation in combination with feature extraction using translation invariant non-negative sparse coding. Our approach simultaneously learns basic motion patterns and estimates the flow field without requiring pretrained motion patterns from ground truth optical flow data. We show on a face expression dataset how this method can preserve weak movements even in the presence of large head movements.