Pattern Recognition and Image Analysis

, Volume 23, Issue 4, pp 481–487 | Cite as

Setup and calibration of a distributed camera system for surveillance of laboratory space

Software and Hardware for Pattern Recognition and Image Analysis
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Abstract

This paper describes the setup and realization of a distributed camera system designed to survey a laboratory area where humans and mobile manipulator robots collaborate jointly. The system consists of 40 industrial grade cameras surveying a 10 m by 10 m area from a top-down perspective, connected via Gigabit Ethernet (GigE) to a cluster of 40 computers for distributed image processing. The cameras were fully calibrated, achieving an average reprojection error of 0.13 pixels for the complete system, which exceeds state-of-the art accuracy. Current long-term testing has the system running with at least 99.994% availability for up to two weeks. Successful application tests of the system were conducted, where it was used to track the movements of robots and humans across the surveyed area.

Keywords

computer vision multi-camera systems multi-camera calibration performance evaluation 

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References

  1. 1.
    E. Carey and V. Rowley, “GigE Vision: video streaming and device control over Ethernet standard,” Tech. Rep. (Automated Imaging Association (AIA), Ann Harbor, MI, 2011).Google Scholar
  2. 2.
    R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd ed. (Cambridge University Press, Cambridge, 2004). doi:10.1016/S0143-8166(01)00145-2CrossRefMATHGoogle Scholar
  3. 3.
    J. Heikkilach. Intellig. 22(10), 1066–1077 (2000). doi: 10.1109/34.879788.Google Scholar
  4. 4.
    G. Kurillo, Z. Li, and R. Bajcsy, “Framework for hierarchical calibration of multi-camera systems for teleimmersion,” in Proc. 2nd Int. Conf. on Immersive Telecommunications IMMERSCOM’09, Ed. by D. Mukherjee, A. Smolic, G. Alregib, and K. Nahrstedt (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (ICST), Berkeley, CA, 2009), pp. 1–6.Google Scholar
  5. 5.
    S. Lanser, C. Zierl, and R. Beutlhauser, “Multibildkalibrierung einer CCD-Kamera,” in Proc. 17th DAGM Symp. Verstehen akustischer und visueller Informationen. Bielefeld, Sept. 13–15, 1995, Ed. by G. Sagerer, S. Posch, and F. Kummert (Springer, Berlin, Heidelberg, 1995), pp. 481–491. doi: 10.1007/978-3-642-79980-8-57.Google Scholar
  6. 6.
    C. L. Ogden, C. D. Fryar, M. D. Carroll, and K. M. Flegal, “Mean body weight, height, and body mass index, United States 1960–2002,” Adv. Data 347, 1–17 (2004).Google Scholar
  7. 7.
    M. Pollefeys, S. N. Sinha, L. Guan, and J.-S. Franco, “Multi-view calibration, synchronization, and dynamic scene reconstruction,” in Multi-Camera Networks: Principles and Applications, Ed. by H. Aghajan and A. Cavallaro (Elsevier, 2008), Chapter 2, pp. 29–75. doi: 10.1016/B978-0-12-374633-7.00004-5.Google Scholar
  8. 8.
    C. Steger, M. Ulrich, and C. Wiedemann, Machine Vision Algorithms and Applications, 1st ed. (Wiley-VCH, Weinheim, 2008).Google Scholar
  9. 9.
    T. Svoboda, D. Martinec, and T. Pajdla, “A convenient multicamera self-calibration for virtual environments,” Presence Teleoperators Virtual Environ. 14(4) 407–422 (2005). doi: 10.1162/105474605774785325.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2013

Authors and Affiliations

  • M. Eggers
    • 1
  • V. Dikov
    • 2
  • C. Mayer
    • 1
  • C. Steger
    • 2
  • B. Radig
    • 1
  1. 1.Intelligent Autonomous Systems Group (IAS)Technische Universität München (TUM)MünchenGermany
  2. 2.MVTec Software GmbHMunichGermany

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