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Robust 3D Marker Localization Using Multi-spectrum Sequences

  • Pengcheng Li
  • Jun Cheng
  • Ruifeng Yuan
  • Wenchuang Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5876)

Abstract

Robust 3D marker localization in different conditions is an open, challenging problem to stereovision systems. For years, many algorithms — using monocular or multiple views; based on visible or infrared sequences — have been proposed to solve this problem. But they all have limitations. In this paper, we propose a novel algorithm for robust 3D marker localization in different conditions, using synchronous visible and infrared (IR) spectrum sequences captured by binocular camera. The main difficulty of the proposed algorithm is how to accurately match the corresponding marked objects in multi-spectrum views. We propose to solve the matching problem by considering geometry constraints, context based features of special designed markers, 3D physical spacial constraints, and etc. Experimental results demonstrated the feasibility of the proposed algorithm.

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References

  1. 1.
    Zhou, H., Hu, H.: Human motion tracking for rehabilitation-a survey. Biomedical Signal Processing and Control 3, 1–18 (2008)CrossRefGoogle Scholar
  2. 2.
    Multon, F., Hoyet, L., Komura, T., Kulpa, R.: Interactive control of physically-valid aerial motion: Application to vr training system for gymnasts. In: Proceedings of the 2007 ACM Symposium on Virtual Reality Software and Technology, pp. 77–80 (2007)Google Scholar
  3. 3.
    Freeman, W., Tanaka, K., Ohta, J., Kyuma, K.: Computer vision for computer games. In: Automatic Face and Gesture Recognition, pp. 100–105 (1995)Google Scholar
  4. 4.
    Michoud, B., Guillou, E., Briceno, H., Bouakaz, S.: Real-time and marker-free 3d motion capture for home entertainment oriented applications. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 678–687. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Tao, Y., Hu, H.: Building a visual tracking system for home-based rehabilitation. In: Proceedings of the 9th Chinese Automation and Computing Society Conference in the UK, England, pp. 343–348 (2003)Google Scholar
  6. 6.
  7. 7.
    Ferrigno, G., Pedotti, A.: Elite: A digital dedicated hardware system for movement analysis via real-time tv signal processing. In: IEEE Transaction on Biomedical Engineering, vol. 32, pp. 943–949 (1985)Google Scholar
  8. 8.
    Sementille, A., Lourenco, L., Brega, J., Rodello, I.: A motion capture system using passive markers. In: Proceedings of the 2004 ACM SIGGRAPH International Conference on Virtual Reality Continuum and its Applications in Industry, pp. 440–447 (2004)Google Scholar
  9. 9.
    Chung, J., Kim, J., Shim, K.: Vision based motion tracking system for interactive entertainment applications. In: TENCON 2005, pp. 1–6 (2005)Google Scholar
  10. 10.
    Gunn, S.: Support vectors machines for classification and regression. In: Technical Report, Image Speech and Intelligent Systems Research Group, University of Southampton (1997)Google Scholar
  11. 11.
    Trinh, P., Ngoc, P., Jo, K.: Color-based face detection using combination of modified local binary patterns and embedded hidden markov models. In: SICE-ICASE International Joint Conference, pp. 18–21 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Pengcheng Li
    • 1
  • Jun Cheng
    • 1
  • Ruifeng Yuan
    • 1
  • Wenchuang Zhao
    • 1
  1. 1.Shenzhen Institute of Advanced Integration TechnologyChinese Academy of Sciences/The Chinese University of Hong KongShenzhenChina

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