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Machine Vision and Applications

, Volume 26, Issue 7–8, pp 955–973 | Cite as

Garment-based motion capture (GaMoCap): high-density capture of human shape in motion

  • Nicoló Biasi
  • Francesco Setti
  • Alessio Del Bue
  • Mattia Tavernini
  • Massimo Lunardelli
  • Alberto Fornaser
  • Mauro Da Lio
  • Mariolino De Cecco
Original Paper

Abstract

This paper presents a new motion capture (MoCap) system, the garment-based motion capture system—GaMoCap. The key feature is the use of an easily wearable garment printed with colour-coded pattern and a generic multicamera setup with standard video cameras. The coded pattern allows a high-density distribution of markers per unit of surface (about 40 markers per 100 cm\(^2\)), avoiding markers-swap errors. The high density of markers reconstructed makes possible a simultaneous reconstruction of shape and motion, which gives several concurrent advantages with respect to the state of the art and providing performances comparable with previous marker-based systems. In particular, we provide effective solutions to counter the soft-tissue artefact which is a common problem for garment-based techniques. This effect is reduced using Point Cluster Technique to filter out the points strongly affected by non-rigid motion. Uncertainty of motion estimation has been experimentally quantified by comparing with a state-of-the-art commercial system and numerically predicted by means of a Monte Carlo Method procedure. The experimental evaluation was performed on three different articulated motions: shoulder, knee and hip flexion-extension. The results shows that for the three motion angles estimated with GaMoCap, the system provides comparable accuracies against a commercial VICON system.

Keywords

3D reconstruction Motion capture Structure-from-motion Multi-camera systems Soft-tissue artefacts 

Notes

Acknowledgments

The authors would like to thank Patrick Olivier, Guy Schofield and Dave Green of Culture Lab, Newcastle University (http://di.ncl.ac.uk/) for providing the VICON system and the support during data acquisition.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Mechanical and Structural EngineeringUniversity of TrentoTrentoItaly
  2. 2.Istituto di Scienze e Tecnologie della Cognizione (ISTC)Consiglio Nazionale delle Ricerche (CNR)TrentoItaly
  3. 3.Visual Geometry and Modelling Lab (VGM), Pattern Analysis and Computer Vision Department (PAVIS)Istituto Italiano di Tecnologia (IIT)GenovaItaly

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