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Distributed Smart Camera Calibration Using Blinking LED

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5259))

Abstract

Smart camera networks are very powerful for various computer vision applications. As a preliminary step in the application, every camera in the scene needs to be calibrated. For most of the calibration algorithms, image point correspondences are needed. Therefore, easy to detect objects can be used like LEDs. Unfortunately, existing LED based calibration methods are highly sensitive to lighting conditions and only perform well in dark conditions. Therefore, in this paper, we propose a robust LED detection method for the calibration process. The main contribution to the robustness of our algorithm is the blinking behavior of the LED, enabling the use of temporal pixel information. Experiments show that accurate LED detection is already possible for a sequence length of three frames. A distributed implementation on a truly embedded smart camera is performed. Finally, a successful spatial calibration is performed with this implemented method.

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References

  1. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  2. Barton-Sweeney, A., Lymberopoulos, D., Savvides, A.: Sensor Localization and Camera Calibration in Distributed Camera Sensor Networks. In: Broadband Communications, Networks and Systems, pp. 1–10 (2006)

    Google Scholar 

  3. Matlab Camera Calibration Toolbox, Caltech Computational Vision (July 2008), http://www.vision.caltech.edu/bouguetj/calib_doc/

  4. Hartley, R.: In Defense of the Eight-Point Algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(6), 580–593 (1997)

    Article  Google Scholar 

  5. Horn, B.: Relative Orientation. International Journal of Computer Vision 4(1), 59–78 (1990)

    Article  Google Scholar 

  6. Longuet-Higgins, H.C.: A Computer Algorithm for Reconstructing a Scene From Two Projections. Nature 293, 133–135 (1981)

    Article  Google Scholar 

  7. Sinha, S.N., Pollefeys, M., McMillan, L.: Camera Network Calibration from Dynamic Silhouettes. IEEE Proceedings of Computer Vision and Pattern Recognition 1, I-195–I-202 (2004)

    Google Scholar 

  8. Svoboda, T., Martinec, D., Pajdla, T.: A Convenient Multi-Camera Self-Calibration for Virtual Environments. Teleoperators and Virtual Environments 14(4) (August 2005)

    Google Scholar 

  9. Longuet-Higgins, H.C.: The Reconstruction of a Scene From Two Projections - Configurations That Defeat the Eight-Point Algorithm. In: IEEE Proceedings of the First Conference on Artificial Intelligence Applications (December 1984)

    Google Scholar 

  10. Maybank, S.J.: The Projective Geometry of Ambiguous Surfaces. Philosophical Transactions: Physical Science and Engineering 332(1623), 1–47 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  11. Yamazoe, H., Utsumi, A., et al.: Geometrical and Temporal Calibration of Multiple Cameras by Using LED Markers for Image Synthesis. In: ICAT (2004)

    Google Scholar 

  12. Nister, D.: An Efficient Solution to the Five-Point Relative Pose Problem. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(6), 756–777 (2004)

    Article  Google Scholar 

  13. Shirmohammadi, B., Taylor, C.: Self Localizing Smart Camera Networks and their Applications to 3D Modeling. In: ACM Sensys / First Workshop on Distributed Smart Cameras (October 2006)

    Google Scholar 

  14. Girod, B., Rabenstein, R., et al.: Signals and Systems. Wiley, Chichester (2001)

    Google Scholar 

  15. Kleihorst, R., Schueler, B., Danilin, A.: Architecture and Applications of Wireless Smart Cameras (Networks). In: Proceedings ICASSP (2007)

    Google Scholar 

  16. Kleihorst, R., et al.: Xetal: A Low-Power High-Performance Smart Camera Processor. IEEE International Symposium on Circ. and Syst. 5, 215–218 (2001)

    Google Scholar 

  17. Abbo, A.A., Kleihorst, R.P., et al.: Xetal-II: A 107 GOPS, 600 mW Massively Parallel Processor for Video Scene Analysis. IEEE Journal of Solid-State Circuits 43, 192–201 (2008)

    Article  Google Scholar 

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© 2008 Springer-Verlag Berlin Heidelberg

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Koch, M., Zivkovic, Z., Kleihorst, R., Corporaal, H. (2008). Distributed Smart Camera Calibration Using Blinking LED. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_22

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  • DOI: https://doi.org/10.1007/978-3-540-88458-3_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88457-6

  • Online ISBN: 978-3-540-88458-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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