Region-based nonparametric optical flow segmentation with pre-clustering and post-clustering

Abstract

A region-based nonparametric video object segmentation over an optical-flow field is proposed to overcome the drawbacks inherited in pixel-based parametric approaches. The key novelties of this approach are: (1) motion field smoothing; (2) pre-clustering and post-clustering. By utilizing both spatial and temporal information extracted from the input video sequence, the raw optical-flow field is partitioned into homogeneous regions, with each region undergoing a common translational motion. Such an objective can be achieved through iterative spatio-temporal processing until the predetermined error-tolerance threshold is met. To facilitate fuzzy c-means clustering, pre-clustering and post-clustering are proposed. Experimental results demonstrate that they also effectively contribute a much improved performance in video object segmentation.