A Low Cost Visual Hull based Markerless System for the Optimization of Athletic Techniques in Outdoor Environments

A. El-Sallam, M. Bennamoun, F. Sohel, J. Alderson, A. Lyttle, T. Warburton

Abstract

We propose a low cost 3D markerless motion analysis system for the optimization of athletic performance during training sessions. The system utilizes eight calibrated and synchronized High Definition (HD) cameras in order to capture a video of an athlete from different viewpoints. An improved kernel density estimation (KDE) based background segmentation algorithm is proposed to segment the athlete’s silhouettes from their background in each video frame. The silhouettes are then reprojected to reconstruct the 3D visual hull (VH) of the athlete. The center of the VH as an approximate representation of the body center of mass is then tracked over a number of frames. A set of motion analysis parameters are finally estimated and compared to the ones obtained by an outdoor state of the art marker-based system (Vicon). The proposed system is aimed at sports such as javelin, pole vault, and long jump and was able to provide comparable results with the Vicon system.

References

  1. Bartlett, R. (2007). Introduction to sports biomechanics: Analysing human movement patterns. Psychology Press.
  2. Benezeth, Y., Jodoin, P., Emile, B., Laurent, H., and Rosenberger, C. (2008). Review and evaluation of commonly-implemented background subtraction algorithms. In Proc. 19th IEEE ICPR conf., pages 1-4.
  3. Bouguet, J. (2010). Camera calibration toolbox for matlab, 2006. URL http://www.vision. caltech.edu/bouguetj.
  4. Callaway, A., Cobb, J., and Jones, I. (2009). A comparison of video and accelerometer based approaches applied to performance monitoring in swimming. International Journal of Sports Science and Coaching, 4(1):139-153.
  5. Elgammal, A., Duraiswami, R., Harwood, D., and Davis, L. (2002). Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. of the IEEE, 90(7):1151-1163.
  6. Hartley, R. I. and Zisserman, A. (2004). Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521540518, 2nd edition.
  7. Laurentini, A. (2003). The visual hull for understanding shapes from contours: a survey. In Proc. 7th IEEE ISSPA Conf., volume 1, pages 25-28.
  8. M. Rossi, e. a. (2012). A novel approach to calculate body segments inertial parameters from dxa and 3d scanners data. 4th International Conference on Computational Methods (ICCM2012).
  9. Moeslund, T., Hilton, A., and Krüger, V. (2006). A survey of advances in vision-based human motion capture and analysis. Comp. vision and image understanding, 104(2):90-126.
  10. Piccardi, M. (2004). Background subtraction techniques: a review. In Proc. EEE SMC Conf., 2004, volume 4, pages 3099-3104.
  11. Radke, R., Andra, S., Al-Kofahi, O., and Roysam, B. (2005). Image change detection algorithms: a systematic survey. IEEE Transactions on Image Processing, 14(3):294-307.
  12. Roetenberg, D. (2006). Inertial and magnetic sensing of human motion. PhD thesis.
  13. Sain, S. (2002). Multivariate locally adaptive density estimation. Computational statistics & data analysis, 39(2):165-186.
  14. Sheikh, Y. and Shah, M. (2005). Bayesian modeling of dynamic scenes for object detection. IEEE PAMI, 27(11):1778-1792.
  15. Svoboda, T., Martinec, D., and Pajdla, T. (2005). A convenient multicamera self-calibration for virtual environments. Presence: Teleoper. Virtual Environ., 14(4):407-422.
  16. Turlach, B. (1993). Bandwidth selection in kernel density estimation: A review. Institut für Statistik und O konometrie, Humboldt-Universität zu Berlin, 19(4):1-33.
  17. Vicon (2010). http://www.vicon.com.
  18. Wand, M. and Jones, M. (1995). Kernel smoothing, volume 60 of monographs on statistics and applied probability. Chapman Hall, New York.
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Paper Citation


in Harvard Style

El-Sallam A., Bennamoun M., Sohel F., Alderson J., Lyttle A. and Warburton T. (2013). A Low Cost Visual Hull based Markerless System for the Optimization of Athletic Techniques in Outdoor Environments . In Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2013) ISBN 978-989-8565-46-4, pages 49-59. DOI: 10.5220/0004291000490059


in Bibtex Style

@conference{grapp13,
author={A. El-Sallam and M. Bennamoun and F. Sohel and J. Alderson and A. Lyttle and T. Warburton},
title={A Low Cost Visual Hull based Markerless System for the Optimization of Athletic Techniques in Outdoor Environments},
booktitle={Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2013)},
year={2013},
pages={49-59},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004291000490059},
isbn={978-989-8565-46-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2013)
TI - A Low Cost Visual Hull based Markerless System for the Optimization of Athletic Techniques in Outdoor Environments
SN - 978-989-8565-46-4
AU - El-Sallam A.
AU - Bennamoun M.
AU - Sohel F.
AU - Alderson J.
AU - Lyttle A.
AU - Warburton T.
PY - 2013
SP - 49
EP - 59
DO - 10.5220/0004291000490059