Automatic Calibration of Stationary Surveillance Cameras in the Wild

  • Guido M. Y. E. Brouwers
  • Matthijs H. Zwemer
  • Rob G. J. Wijnhoven
  • Peter H. N. de With
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9914)


We present a fully automatic camera calibration algorithm for monocular stationary surveillance cameras. We exploit only information from pedestrians tracks and generate a full camera calibration matrix based on vanishing-point geometry. This paper presents the first combination of several existing components of calibration systems from literature. The algorithm introduces novel pre- and post-processing stages that improve estimation of the horizon line and the vertical vanishing point. The scale factor is determined using an average body height, enabling extraction of metric information without manual measurement in the scene. Instead of evaluating performance on a limited number of camera configurations (video seq.) as in literature, we have performed extensive simulations of the calibration algorithm for a large range of camera configurations. Simulations reveal that metric information can be extracted with an average error of 1.95 % and the derived focal length is more accurate than the reported systems in literature. Calibration experiments with real-world surveillance datasets in which no restrictions are made on pedestrian movement and position, show that the performance is comparable (max. error 3.7 %) to the simulations, thereby confirming feasibility of the system.


Automatic camera calibration Vanishing points 


  1. 1.
    Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using K-shortest paths pptimization. IEEE Trans. Pattern Anal. Mach. Intell. (2011)Google Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893, June 2005Google Scholar
  3. 3.
    Dubska, M., Herout, A., Sochor, J.: Automatic camera calibration for traffic understanding. In: Proceedings of the British Machine Vision Conference. BMVA Press (2014)Google Scholar
  4. 4.
    Faugeras, O.D., Toscani, G.: The calibration problem for stereo. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1986, Miami Beach, FL, 22–26 June 1986, pp. 15–20. IEEE (1986). IEEE Publ. 86CH2290-5Google Scholar
  5. 5.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004). ISBN 0521540518CrossRefzbMATHGoogle Scholar
  6. 6.
    Hartley, R.I.: Self-calibration from multiple views with a rotating camera. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 800, pp. 471–478. Springer, Heidelberg (1994). doi: 10.1007/3-540-57956-7_52 CrossRefGoogle Scholar
  7. 7.
    Huang, S., Ying, X., Rong, J., Shang, Z., Zha, H.: Camera calibration from periodic motion of a pedestrian. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016Google Scholar
  8. 8.
    Krahnstoever, N., Mendonca, P.: Bayesian autocalibration for surveillance. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1858–1865, October 2005Google Scholar
  9. 9.
    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, December 2009Google Scholar
  10. 10.
    Liu, J., Collins, R.T., Liu, Y.: Surveillance camera autocalibration based on pedestrian height distributions. In: British Machine Vision Conference (BMVC) (2011)Google Scholar
  11. 11.
    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, January 2013Google Scholar
  12. 12.
    Lv, F., Zhao, T., Nevatia, R.: Self-calibration of a camera from video of a walking human. In: Proceedings of 16th International Conference on Pattern Recognition, 2002, vol. 1, pp. 562–567 (2002)Google Scholar
  13. 13.
    Lv, F., Zhao, T., Nevatia, R.: Camera calibration from video of a walking human. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1513–1518 (2006)CrossRefGoogle Scholar
  14. 14.
    Maybank, S.J., Faugeras, O.D.: A theory of self-calibration of a moving camera. Int. J. Comput. Vision 8(2), 123–151 (1992). CrossRefGoogle Scholar
  15. 15.
    Micusik, B., Pajdla, T.: Simultaneous surveillance camera calibration and foot-head homology estimation from human detections. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1562–1569, June 2010Google Scholar
  16. 16.
    Millar, W.: Distribution of body weight and height: comparison of estimates based on self-reported and observed measures. J. Epidemiol. Community Health 40(4), 319–323 (1986)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Orghidan, R., Salvi, J., Gordan, M., Orza, B.: Camera calibration using two or three vanishing points. In: 2012 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 123–130, September 2012Google Scholar
  18. 18.
    Possegger, H., Rther, M., Sternig, S., Mauthner, T., Klopschitz, M., Roth, P.M., Bischof, H.: Unsupervised calibration of camera networks and virtual PTZ cameras. In: Proceedings of Computer Vision Winter Workshop (CVWW) (2012). Supplemental Video, Dataset, CodeGoogle Scholar
  19. 19.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Guido M. Y. E. Brouwers
    • 1
  • Matthijs H. Zwemer
    • 1
    • 2
  • Rob G. J. Wijnhoven
    • 1
  • Peter H. N. de With
    • 2
  1. 1.ViNotion B.V.EindhovenThe Netherlands
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands

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