Multiple-class spatiotemporal flow estimation using a modified neural gas algorithm

Abstract

The problem of estimating optical flow fields corresponding to multiple moving objects in a spatiotemporal image sequence is addressed. A modified version of the neural gas (NG) unsupervised learning algorithm is used to implement a nonlinear interpolation strategy to overcome the aperture problem encountered during local motion estimation. Local motion constraints are formulated, and the best information over four point pairs is used to produce a single motion estimate. Wherever the aperture problem is encountered the minimum-norm estimate is produced. These local estimates are then refined using the modified NG. NG provides a framework for the fusion of local incomplete motion information into complete global estimates. Due to the self-organizing nature of NG the number of motion classes need not be specified a priori. The technique leads to generation of an optical flow field without the smearing of flow fields encountered in regularization-based techniques. Motion estimation results obtained on synthetic natural image sequences are shown.