3D pose estimation and motion analysis of the articulated human hand-forearm limb in an industrial production environment

  • Authors:

  • Markus Hahn;Björn Barrois;Lars Krüger;Christian Wöhler;Gerhard Sagerer;Franz Kummert

  • Affiliations:

  • Daimler Group Research, Environment Perception, Ulm, Germany D-89013;Daimler Group Research, Environment Perception, Ulm, Germany D-89013;Daimler Group Research, Environment Perception, Ulm, Germany D-89013;Image Analysis Group, Dortmund University of Technology, Dortmund, Germany D-44227;Applied Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany D-33501;Applied Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany D-33501

  • Venue:
  • 3D Research
  • Year:
  • 2010

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Abstract

This study introduces an approach to model-based 3D pose estimation and instantaneous motion analysis of the human hand-forearm limb in the application context of safe human-robot interaction. 3D pose estimation is performed using two approaches: The Multiocular Contracting Curve Density (MOCCD) algorithm is a top-down technique based on pixel statistics around a contour model projected into the images from several cameras. The Iterative Closest Point (ICP) algorithm is a bottom-up approach which uses a motion-attributed 3D point cloud to estimate the object pose. Due to their orthogonal properties, a fusion of these algorithms is shown to be favorable. The fusion is performed by a weighted combination of the extracted pose parameters in an iterative manner. The analysis of object motion is based on the pose estimation result and the motion-attributed 3D points belonging to the hand-forearm limb using an extended constraint-line approach which does not rely on any temporal filtering. A further refinement is obtained using the Shape Flow algorithm, a temporal extension of the MOCCD approach, which estimates the temporal pose derivative based on the current and the two preceding images, corresponding to temporal filtering with a short response time of two or at most three frames. Combining the results of the two motion estimation stages provides information about the instantaneous motion properties of the object.Experimental investigations are performed on real-world image sequences displaying several test persons performing different working actions typically occurring in an industrial production scenario. In all example scenes, the background is cluttered, and the test persons wear various kinds of clothes. For evaluation, independently obtained ground truth data are used.