Advertisement

Auto-calibration of Non-overlapping Multi-camera CCTV Systems

  • Cristina Picus
  • Roman Pflugfelder
  • Branislav Micusik
Part of the Studies in Computational Intelligence book series (SCI, volume 409)

Abstract

Deployment of existing vision approaches in camera networks for applications such as human tracking show a large gap between user expectation and current results. Calibrated cameras could push these approaches closer to applicability, as physical constraints greatly complement the ill-posed acquisition process. Calibrated cameras promise also new applications as spatial relationships among cameras and the environment capture additional information. However, a convenient calibration is still a challenge on its own. This paper presents a novel calibration framework for large networks including non-overlapping cameras. The framework purely relies on visual information coming from walking people. Since non-overlapping scenarios make point correspondences impossible, time constancy of a person’s motion introduces the missing complementary information. The framework obtains calibrated cameras starting from single camera calibration thereby bringing the problem to a reduced form suitable for multi-view calibration. It extends the standard bundle adjustment by a smoothness constraint to avoid the ill-posed problem arising from missing point correspondences. The stratified optimization suppresses the danger to get stuck in local minima. Experiments with synthetic and real data validate the approach.

Keywords

Camera Calibration Camera Parameter Bundle Adjustment Human Detection Smoothness Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lv, F., Zhao, T., Nevatia, R.: Self-calibration of a camera from video of a walking human. In: ICPR (2002)Google Scholar
  2. 2.
    Krahnstoever, N., Mendonca, P.R.S.: Bayesian autocalibration for surveillance. In: ICCV, pp. II:1858–II:1865 (2005)Google Scholar
  3. 3.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)Google Scholar
  4. 4.
    Fischler, M., Bolles, R.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24, 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Rahimi, A., Dunagan, B., Darrell, T.: Simultaneous calibration and tracking with a network of non-overlapping sensors. In: CVPR, pp. (I):187–(I):194 (2004)Google Scholar
  6. 6.
    McGlone (ed.): Manual of Photogrammetry. ASPRS (2004)Google Scholar
  7. 7.
    Szeliski, R.: Computer Vision: Algorithms and Applications. Springer (2009)Google Scholar
  8. 8.
    Baker, P., Aloimonos, Y.: Complete calibration of a multi-camera network. In: Proceedings of IEEE Workshop on Omnidirectional Vision, pp. 134–141 (2000)Google Scholar
  9. 9.
    Svoboda, T., Martinec, D., Pajdla, T.: A convinient multi-camera self-calibration for virtual environments. PRESENCE: Teleoperators and Virtual Environments 14(4), 407–422 (2005)CrossRefGoogle Scholar
  10. 10.
    Lee, L., Romano, R., Stein, G.: Monitoring activities from multiple video streams: Establishing a common coordinate frame. IEEE Transaction on Pattern Analysis and Machine Intelligence (PAMI) 22(8), 758–767 (2000)CrossRefGoogle Scholar
  11. 11.
    Stein, G.: Tracking from multiple view points: Self-calibration of space and time. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1521–1527 (1999)Google Scholar
  12. 12.
    Jaynes, C.: Multi-view calibration from planar motion trajectories. Image and Vision Computing 22(7), 535–550 (2004)CrossRefGoogle Scholar
  13. 13.
    Thaler, M., Mandrzinger, R.: Automatic inter-image homography estimation from person detections. In: 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 456–461 (September 2010)Google Scholar
  14. 14.
    Kahn, S., Shah, M.: Consistent labeling of tracked objects in multiple cameras with overlapping fields of view. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(10), 1355–1360 (2003)CrossRefGoogle Scholar
  15. 15.
    Funiak, S., Guestrin, C., Paskin, M., Sukthankar, R.: Distributed localization of networked cameras. In: Proceedings of the 5th International Conference on Information Processing in Sensor Networks (IPSN), Nashville, pp. 34–42 (April 2006)Google Scholar
  16. 16.
    Sheikh, Y., Li, X., Shah, M.: Trajectory association across non-overlapping moving cameras in planar scenes. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–7 (June 2007)Google Scholar
  17. 17.
    Rudoy, M., Rohrs, C.E.: Simultaneous sensor calibration and path estimation. In: Proc. IEEE Asilomar Conference on Signals, Systems, and Computers (2006)Google Scholar
  18. 18.
    Ellis, T.J., Makris, D., Black, J.K.: Learning a multi-camera topology. In: Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 165–171 (2003)Google Scholar
  19. 19.
    Tieu, K., Dalley, G., Grimson, W.E.L.: Inference of non-overlapping camera network topology by measuring statistical dependence. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1842–1849 (2005)Google Scholar
  20. 20.
    van den Hengel, A., Dick, A., Hill, R.: Activity topology estimation for large networks of cameras. In: IEEE International Conference on Video and Signal Based Surveillance, AVSS 2006, p. 44 (November 2006)Google Scholar
  21. 21.
    Devarajan, D., Cheng, Z., Radke, R.: Calibrating distributed camera networks. Proceedings of the IEEE 96(10), 1625–1639 (2008)CrossRefGoogle Scholar
  22. 22.
    Micusik, B., Pajdla, T.: Simultaneous surveillance camera calibration and foot-head homology estimation from human detections. In: CVPR (2010)Google Scholar
  23. 23.
    Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle Adjustment – A Modern Synthesis (chapter Bundle adjustment - a modern synthesis). In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) ICCV-WS 1999. LNCS, vol. 1883, pp. 298–375. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  24. 24.
    Beleznai, C., Bischof, H.: Fast human detection in crowded scenes by contour integration and local shape estimation. In: CVPR (2009)Google Scholar
  25. 25.
    Liebelt, J., Schmid, C., Schertler, K.: Viewpoint-independent object class detection using 3D feature maps. In: CVPR (2008)Google Scholar
  26. 26.
    Toshev, A., Makadia, A., Daniilidis, K.: Shape-based object recognition in videos using 3D synthetic object models. In: CVPR (2009)Google Scholar
  27. 27.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004)Google Scholar
  28. 28.
    Bai, Z., Demmel, J., Dongarra, J., Ruhe, A., van der Vorst, H. (eds.): Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide. SIAM (2000)Google Scholar
  29. 29.
    Grammont, L., Higham, N.J., Tisseur, F.: A framework for analyzing nonlinear eigenproblems and parametrized linear systems. Linear Algebra and its Applications (2010)Google Scholar
  30. 30.
    Fitzgibbon, A.W.: Simultaneous linear estimation of multiple view geometry and lens distortion. In: CVPR, pp. (I):125–(I):132 (2001)Google Scholar
  31. 31.
    Micusik, B., Pajdla, T.: Structure from motion with wide circular field of view cameras. PAMI 28(7) (2006)Google Scholar
  32. 32.
    Kukelova, Z., Bujnak, M., Pajdla, T.: Polynomial eigenvalue solutions to the 5-pt and 6-pt relative pose problems. In: BMVC (2008)Google Scholar
  33. 33.
    Bujnak, M., Kukelova, Z., Pajdla, T.: 3D reconstruction from image collections with a single known focal length. In: ICCV (2009)Google Scholar
  34. 34.
    Micusik, B.: Relative pose problem for non-overlapping surveillance cameras with known gravity vector. In: CVPR (2011)Google Scholar
  35. 35.
    Boutry, G., Elad, M., Golub, G.H., Milanfar, P., Milanfar, G.H.G.P.: The generalized eigenvalue problem for non-square pencils using a minimal perturbation approach. SIAM J. Matrix Anal. Appl. 27, 582–601 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  36. 36.
    Pflugfelder, R., Bischof, H.: Localization and trajectory reconstruction in surveillance cameras with non-overlapping views. PAMI 32(4), 709–721 (2009)CrossRefGoogle Scholar
  37. 37.
    Lourakis, M.I.A., Argyros, A.A.: SBA: A Software Package for Generic Sparse Bundle Adjustment. ACM Trans. Math. Software 36(1), 1–30 (2009)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Kalal, Z., Matas, J., Mikolajczyk, K.: P-N Learning: Bootstrapping Binary Classifiers by Structural Constraints. In: CVPR (2010)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Cristina Picus
    • 1
  • Roman Pflugfelder
    • 1
  • Branislav Micusik
    • 1
  1. 1.AIT Austrian Institute of TechnologyViennaAustralia

Personalised recommendations