Advertisement

Real-Time Swimmer Tracking on Sparse Camera Array

  • Paavo NevalainenEmail author
  • M. Hashem Haghbayan
  • Antti Kauhanen
  • Jonne Pohjankukka
  • Mikko-Jussi Laakso
  • Jukka Heikkonen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10163)

Abstract

A swimmer detection and tracking is an essential first step in a video-based athletics performance analysis. A real-time algorithm is presented, with the following capabilities: performing the planar projection of the image, fading the background to protect the intimacy of other swimmers, framing the swimmer at a specific swimming lane, and eliminating the redundant video stream from idle cameras. The generated video stream is a basis for further analysis at the batch-mode. The geometric video transform accommodates a sparse camera array and enables geometric observations of swimmer silhouette. The tracking component allows real-time feedback and combination of different video streams to a single one. Swimming cycle registration algorithm based on markerless tracking is presented. The methodology allows unknown camera positions and can be installed in many types of public swimming pools.

Keywords

Athletics Swimming Body motion tracking Camera calibration Background subtraction Video processing Silhouette registration Movement cycle registration 

Notes

Acknowledgements

The project is a joint venture of University of Turku IT department and Sports Academy of Turku region and it has been funded by city of Turku, National Olympic Committee, Finnish Swimming Federation, Urheiluopistosäätiö and University of Turku. Machine Technology Center Turku Ltd. participated to instrumentation.

References

  1. 1.
  2. 2.
    Nevalainen, P., Kauhanen, A., Raduly-Baka, C., Heikkonen, J.: Video based swimming analysis for fast feedback, In: Proceedings of International Conference on Pattern Recognition Applications and Methods (ICPRAM2016), pp. 457–466 (2016)Google Scholar
  3. 3.
    Christodoulidis, A., Delibasis, K.K., Maglogiannis, I.: Near real-time human silhouette and movement detection in indoor environments using fixed cameras. In: Proceedings of the 5th International Conference on Pervasive Technologies Related to Assistive Environments (PETRA 2012) (2012)Google Scholar
  4. 4.
    Choudhury, S.D., Tjahjadi, T.: Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors. Pattern Recogn. 45, 3414–3426 (2012)CrossRefGoogle Scholar
  5. 5.
    Dadashi, F., Millet, G., Aminian, K.: Inertial measurement unit and biomechanical analysis of swimming: an update. Sportmedizin 61, 21–26 (2013)Google Scholar
  6. 6.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000)CrossRefGoogle Scholar
  7. 7.
    Hartikainen, J., Seppänen, M., Särkkä, S.: State-space inference for non-linear latent force models with application to satellite orbit prediction. In: CoRR (2012)Google Scholar
  8. 8.
    Sedlazeck, A., Koch, R.: Perspective and non-perspective camera models in underwater imaging – overview and error analysis. In: Dellaert, F., Frahm, J.-M., Pollefeys, M., Leal-Taixé, L., Rosenhahn, B. (eds.) Outdoor and Large-Scale Real-World Scene Analysis. LNCS, vol. 7474, pp. 212–242. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-34091-8_10 CrossRefGoogle Scholar
  9. 9.
    Makoto, H.S., Kimura, M., Yaguchi, S., Inamoto, N.: View interpolation of multiple cameras based on projective geometry. In: International Workshop on Pattern Recognition and Understanding for Visual Information (2002)Google Scholar
  10. 10.
    Ceseracciu, E.: New frontiers of Markerless Motion Capture: Application to Swim Biomechanics and Gait Analysis. Padova University, Padua (2011)Google Scholar
  11. 11.
    Haner, S., Svärm, L., Ask, E., Heyden, A.: Joint under and over water calibration of a swimmer tracking system. In: Proceedings of the International Conference on Pattern Recognition Applications and Methods, pp. 142–149 (2015)Google Scholar
  12. 12.
    Heikkilä, J., Silven, O.: A four-step camera calibration procedure with implicit image correction. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1106–1112 (1997)Google Scholar
  13. 13.
    Sportsmotion: A motion analysis system (2011–2015). http://www.sportsmotion.com/
  14. 14.
    Dartfish: Dartfish video analysis tool (2011–2015). http://www.sportmanitoba.ca/page.php?id=116
  15. 15.
    Kirmizibayrak, J., Honorio, J., Xiaolong, J., Russell, M., Hahn, J.K.: Digital analysis and visualization of swimming motion. Int. J. Virtual Real. 10, 9–16 (2011)Google Scholar
  16. 16.
    Bideau, B.: Biomechanics and medicine in swimming IX. In: Chatard, J.-C. (ed.) IXth International World Symposium on Biomechanics and Medicine in Swimming, pp. 52–53 (2003)Google Scholar
  17. 17.
    Luo, H.-G., Zhu, L.-M., Ding, H.: Camera calibration with coplanar calibration board near parallel to the imaging plane. Sens. Actuators A: Phys. 132, 480–486 (2006)CrossRefGoogle Scholar
  18. 18.
    Kannala, J., Heikkilä, J., Brandt, S.S.: Geometric camera calibration. In: Wiley Encyclopedia of Computer Science and Engineering (2008)Google Scholar
  19. 19.
    Bottoni, A., et al.: Technical skill differences in stroke propulsion between high level athletes in triathlon and top level swimmers. J. Hum. Sport Exerc. 6(2), 351–362 (2011)CrossRefGoogle Scholar
  20. 20.
    Bouguet, J.Y.: Camera calibration toolbox for Matlab (2008)Google Scholar
  21. 21.
    Mullane, S.L., Justham, L.M., West, A.A., Conway, P.P.: Design of an end-user centric information interface from data-rich. Procedia Eng. 2, 2713–2719 (2010)CrossRefGoogle Scholar
  22. 22.
    Martin, N., Roy, S.: Fast view interpolation from stereo: simpler can be better. In: Proceedings of 3DPTV 2008. The Fourth International Symposium on 3-D Data Processing, Visualization and Transmission, Georgia Institute of Technology, Atlanta, GA, USA (2008)Google Scholar
  23. 23.
    Rocklin, M., Terrel, A.R.: Symbolic statistics with SymPy. Comput. Sci. Eng. 14(3), 88–93 (2012)CrossRefGoogle Scholar
  24. 24.
    Dorst, L., Fontijne, D., Mann, S.: Geometric Algebra for Computer Science: An Object-Oriented Approach to Geometry (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Paavo Nevalainen
    • 1
    Email author
  • M. Hashem Haghbayan
    • 1
  • Antti Kauhanen
    • 2
  • Jonne Pohjankukka
    • 1
  • Mikko-Jussi Laakso
    • 1
  • Jukka Heikkonen
    • 1
  1. 1.Department of Information TechnologyUniversity of TurkuTurkuFinland
  2. 2.Sport Academy of Turku RegionTurkuFinland

Personalised recommendations