3D Marker Data For Motion Recognition

Abstract

Accurate human motion models are a prerequisite for most applications dealing with the tracking, reconstruction or recognition of human motions. Often a uniform model is used, approximating the average of the evaluated subjects. However we expect most applications can be improved by using individual models for each subject with its personal body masses and features. Hence we propose an algorithm for automatic reconstruction of anatomical features of subjects from labeled 3D marker data by a parameterized generic model. Our main contribution is a novel approach for automatically estimating skeletons of individual subjects and to transform them to a human body model by preserving its relative configuration. We show that a more accurate model can help in context of motion recognition by improving standard motion reconstruction with regard to its quantity and quality.