Machine Vision and Applications

, Volume 18, Issue 3–4, pp 249–260 | Cite as

Probabilistic-topological calibration of widely distributed camera networks

  • Norimichi Ukita
Special Issue


We propose a method for estimating the topology of distributed cameras, which can provide useful information for multi-target tracking in a wide area, without object identification among the fields of view (FOVs) of the cameras. In our method, each camera first detects objects in its observed images independently in order to obtain the positions/times where/when the objects enter/exit its FOV. Each obtained data is tentatively paired with all other data detected before the data is observed. A transit time between each paired data and their xy coordinates are then computed. Based on classifying the distribution of the transit times and the xy coordinates, object routes between FOVs can be detected. The classification is achieved by simple and robust vector quantization. The detected routes are then categorized to acquire the probabilistic-topological information of distributed cameras. In addition, offline tracking of observed objects can be realized by means of the calibration process. Experiments demonstrated that our method could automatically estimate the topological relationships of the distributed cameras and the object transits among them.


Distributed camera calibration Route between fields of view Object tracking 


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  1. 1.
    Cai Q., Aggarwal J.K. (1999) Tracking human motion in structured environments using a distributed camera system. PAMI 21(11): 1241–1247Google Scholar
  2. 2.
    Mittal A., Davis L.S. (2003) M2Tracker: a multi-view approach to segmenting and tracking people in a cluttered scene. IJCV 51(3): 189–203CrossRefGoogle Scholar
  3. 3.
    Ng K.C., Ishiguro H., Trivedi M.M., Sogo T. (2004) an integrated surveillance system-human tracking and view synthesis using multiple omni-directional vision sensors. IVC 22(7): 551–561Google Scholar
  4. 4.
    Ukita N., Matsuyama T. (2005) Real-time cooperative multi-target tracking by communicating active vision agents. CVIU 97(2): 137–179Google Scholar
  5. 5.
    Collins R., Amidi, O., Kanade, T.: An active camera system for acquiring multi-view video. In: Proceedings of ICIP, pp. 517–520 (2002)Google Scholar
  6. 6.
    Azarbayejani A., Pentland A. (1996) Real-time self-calibrating stereo person tracking using 3-D shape estimation from blob features. In: Proceedings of ICPR 3, 627–632Google Scholar
  7. 7.
    Chen, X., Davis, J., Slusallek, P.: Wide area camera calibration using virtual calibration objects. In: Proceedings of CVPR 2, 5200–527 (2000)Google Scholar
  8. 8.
    Lee L., Romano R., Stein G. (2000) Monitoring activities from multiple video streams: establishing a common coordinate frame. PAMI 22(8): 758–767Google Scholar
  9. 9.
    Collins R., Lipton A., Fujiyoshi H., Kanade T. (2001) Algorithms for cooperative multisensor surveillance. In: Proceedings of the IEEE 89(10): 1456–1477Google Scholar
  10. 10.
    Pasula, H., Russell, S., Ostland, M., Ritov, Y.: Tracking many objects with many sensors. In: Proceedings of IJCAI, 1160–1171 (1999)Google Scholar
  11. 11.
    Kettnaker, V., Zabih, R.: Bayesian multi-camera surveillance. In: Proceedings of CVPR, pp. 253–259 (1999)Google Scholar
  12. 12.
    Boyd, J.E., Meloche, J., Vardi, Y.: Statistical tracking in video traffic surveillance. In: Proceedings of ICCV, pp. 163–168 (1999)Google Scholar
  13. 13.
    Javed, O., Rasheed, Z., Alatas, O., Shah, M.: KNIGHT: A real time surveillance system for multiple and non-overlapping cameras. In: Proceedings of ICME, pp. 649–652 (2003)Google Scholar
  14. 14.
    Davis, J., Chen, X.: Calibrating pan-tilt cameras in wide-area surveillance networks. In: Proceedings of ICCV2003, pp. 144–149 (2003)Google Scholar
  15. 15.
    Javed, O., Rasheed, Z., Shafique, K., Shah, M.: Tracking across multiple cameras with disjoint views. In: Proceedings of ICCV, pp. 952–957 (2003)Google Scholar
  16. 16.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: WallFlower: Principles and practice of background maintenance. In: Proceedings of ICCV, pp. 255–261 (1999)Google Scholar
  17. 17.
    Vermaak, J., Doucet, A., Perez, P.: Maintaining multi-modality through mixture tracking. In: Proceedings of ICCV, pp. 1110–1116 (2003)Google Scholar
  18. 18.
    Makris, D., Ellis, T., Black, J.: Bridging the gaps between cameras. In: Proceedings of CVPR 2, 205–210 (2004)Google Scholar
  19. 19.
    Linde Y., Buzo A., Gray R.M. (1980) An algorithm for vector quantizer design. IEEE Trans. on Comm. 28(1): 84–95CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Graduate School of Information ScienceNara Institute of Science and Technology (NAIST)NaraJapan

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