Hierarchical 3D Pose Estimation for Articulated Human Body Models from a Sequence of Volume Data

Abstract

This contribution describes a camera-based approach to fully automatically extract the 3D motion parameters of persons using a model based strategy. In a first step a 3D body model of the person to be tracked is constructed automatically using a calibrated setup of sixteen digital cameras and a monochromatic background. From the silhouette images the 3D shape of the person is determined using the shape-from-silhouette approach. This model is segmented into rigid body parts and a dynamic skeleton structure is fit. In the second step the resulting movable, personalized body template is exploited to estimate the 3D motion parameters of the person in arbitrary poses. Using the same camera setup and the shape-from-silhouette approach a sequence of volume data is captured to which the movable body template is fit. Using a modified ICP algorithm the fitting is performed in a hierarchical manner along the the kinematic chains of the body model. The resulting sequence of motion parameters for the articulated body model can be used for gesture recognition, control of virtual characters or robot manipulators.