Point cloud video object segmentation using a persistent supervoxel world-model

Abstract

Robust visual tracking is an essential precursor to understanding and replicating human actions in robotic systems. In order to accurately evaluate the semantic meaning of a sequence of video frames, or to replicate an action contained therein, one must be able to coherently track and segment all observed agents and objects. This work proposes a novel online point cloud based algorithm which simultaneously tracks 6DoF pose and determines spatial extent of all entities in indoor scenarios. This is accomplished using a persistent supervoxel world-model which is updated, rather than replaced, as new frames of data arrive. Maintenance of a world model enables general object permanence, permitting successful tracking through full occlusions. Object models are tracked using a bank of independent adaptive particle filters which use a supervoxel observation model to give rough estimates of object state. These are united using a novel multi-model RANSAC-like approach, which seeks to minimize a global energy function associating world-model supervoxels to predicted states. We present results on a standard robotic assembly benchmark for two application scenarios - human trajectory imitation and semantic action understanding - demonstrating the usefulness of the tracking in intelligent robotic systems.