Motion segmentation based on perceptual organization of spatio-temporal volumes

The role of perceptual organization in motion analysis has heretofore been minimal. In this work we demonstrate that the use of perceptual organization principles of temporal coherence (common fate) and spatial proximity can result in a robust motion segmentation algorithm that is able to handle drastic illumination changes, occlusion events, and multiple moving objects, without the use of object models. The adopted algorithm does not employ the traditional frame by frame motion analysis, but rather treats the image sequence as a single 3D spatio-temporal block of data. We describe motion using spatio-temporal surfaces, which we, in turn, describe as compositions of finite planar patches. These planar patches, referred to as temporal envelopes, capture the local nature of the motions. We detect these temporal envelopes using 3D-edge detection followed by Hough transform, and represent them with convex hulls. We present a graph-based method to group these temporal envelopes arising from one object based on Gestalt organizational principles. A probabilistic Bayesian network quantifies the saliencies of the relationships between temporal envelopes. We present results on sequences with multiple moving persons, significant occlusions, and scene illumination changes.