Scene Understanding for Auto-Calibration of Surveillance Cameras

  • Lucas Teixeira
  • Fabiola Maffra
  • Atta Badii
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8888)

Abstract

In the last decade, several research results have presented formulations for the auto-calibration problem. Most of these have relied on the evaluation of vanishing points to extract the camera parameters. Normally vanishing points are evaluated using pedestrians or the Manhattan World assumption i.e. it is assumed that the scene is necessarily composed of orthogonal planar surfaces. In this work, we present a robust framework for auto-calibration, with improved results and generalisability for real-life situations. This framework is capable of handling problems such as occlusions and the presence of unexpected objects in the scene. In our tests, we compare our formulation with the state-of-the-art in auto-calibration using pedestrians and Manhattan World-based assumptions. This paper reports on the experiments conducted using publicly available datasets; the results have shown that our formulation represents an improvement over the state-of-the-art.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Senst, T., Eiselein, V., Badii, A., Einig, M., Keller, I., Sikora, T.: A decentralized privacy-sensitive video surveillance framework. In: 2013 18th International Conference on Digital Signal Processing (DSP), pp. 1–6 (2013)Google Scholar
  2. 2.
    Coughlan, J.M., Yuille, A.L.: Manhattan world: compass direction from a single image by bayesian inference. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 941–947 (1999)Google Scholar
  3. 3.
    Liu, J., Collins, R., Liu, Y.: Robust autocalibration for a surveillance camera network. In: 2013 IEEE Workshop on Applications of Computer Vision (WACV), pp. 433–440 (2013)Google Scholar
  4. 4.
    Liu, J., Collins, R.T., Liu, Y.: Surveillance camera autocalibration based on pedestrian height distributions. In: Proc. British Machine Vision Conference (2011)Google Scholar
  5. 5.
    Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: A benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 304–311 (2009)Google Scholar
  6. 6.
    Kusakunniran, W., Li, H., Zhang, J.: A direct method to self-calibrate a surveillance camera by observing a walking pedestrian. In: Digital Image Computing: Techniques and Applications, DICTA 2009, pp. 250–255 (2009)Google Scholar
  7. 7.
    Lv, F., Zhao, T., Nevatia, R.: Camera calibration from video of a walking human. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (2006)Google Scholar
  8. 8.
    Wildenauer, H., Hanbury, A.: Robust camera self-calibration from monocular images of manhattan worlds. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2831–2838 (2012)Google Scholar
  9. 9.
    Liebowitz, D.: Camera Calibration and Reconstruction of Geometry from Images. PhD thesis, University of Oxford, Dept. Engineering Science, D.Phil. thesis (2001)Google Scholar
  10. 10.
    Evangelio, R.H.: Background Substraction for the Detection of Moving and Static Objects in Video Surveillance. PhD thesis, Technische Universität Berlin (2014)Google Scholar
  11. 11.
    Vedaldi, A., Soatto, S.: Quick shift and kernel methods for mode seeking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 705–718. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 1222–1239 (2001)CrossRefGoogle Scholar
  13. 13.
    Kolmogorov, V., Zabin, R.: What energy functions can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 147–159 (2004)CrossRefGoogle Scholar
  14. 14.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1124–1137 (2004)CrossRefGoogle Scholar
  15. 15.
    Tsai, R.: A versatile camera calibration technique for high-accuracy 3d machine vision metrology using off-the-shelf tv cameras and lenses. IEEE Journal of Robotics and Automation 3, 323–344 (1987)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lucas Teixeira
    • 1
  • Fabiola Maffra
    • 1
  • Atta Badii
    • 1
  1. 1.Intelligent Systems Research LaboratoryUniversity of ReadingUK

Personalised recommendations