Tracking Objects Across Cameras by Incrementally Learning Inter-camera Colour Calibration and Patterns of Activity

  • Andrew Gilbert
  • Richard Bowden
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3952)


This paper presents a scalable solution to the problem of tracking objects across spatially separated, uncalibrated, non-overlapping cameras. Unlike other approaches this technique uses an incremental learning method, to model both the colour variations and posterior probability distributions of spatio-temporal links between cameras. These operate in parallel and are then used with an appearance model of the object to track across spatially separated cameras. The approach requires no pre-calibration or batch preprocessing, is completely unsupervised, and becomes more accurate over time as evidence is accumulated.


Appearance Model Colour Similarity Observation Likelihood Coarse Quantisation Valid Link 
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.


  1. 1.
    Cai, Q., Agrarian, J.: Tracking Human Motion using Multiple Cameras. In: Proc. International Conference on Pattern Recognition, pp. 67–72 (1996)Google Scholar
  2. 2.
    Kelly, P., Katkere, A., Kuramura, D., Moezzi, S., Chatterjee, S.: An Architecture for Multiple Perspective Interactive Video. In: Proc. of the 3rd ACE International Conference on Multimedia, pp. 201–212 (1995)Google Scholar
  3. 3.
    Chang, T., Gong, S.: Bayesian Modality Fusion for Tracking Multiple People with a Multi-Camera System. In: Proc. European Workshop on Advanced Video-based Surveillance Systems (2001)Google Scholar
  4. 4.
    Kettnaker, V., Zabih, R.: Bayesian Multi-Camera Surveillance. In: Proc. IEEE Computer Vision and Pattern Recognition, pp. 253–259 (1999)Google Scholar
  5. 5.
    Huang, T., Russell, S.: Object Identification in a Bayesian Context. In: Proc. International Joint Conference on Artificial Intelligence (IJCAI 1997), Nagoya, Japan, pp. 1276–1283 (1997)Google Scholar
  6. 6.
    Javed, O., Rasheed, Z., Shafique, K., Shah, M.: Tracking Across Multiple Cameras with Disjoint Views. In: Proc. IEEE International Conference on Computer Vision, pp. 952–957 (2003)Google Scholar
  7. 7.
    Dick, A., Brooks, M.: A Stochastic Approach to Tracking Objects Across Multiple Cameras. In: Australian Conference on Artificial Intelligence, pp. 160–170 (2004)Google Scholar
  8. 8.
    KaewTrakulPong, P., Bowden, R.: A Real-time Adaptive Visual Surveillance System for Tracking Low Resolution Colour Targets in Dynamically Changing Scenes. Journal of Image and Vision Computing 21(10), 913–929 (2003)CrossRefGoogle Scholar
  9. 9.
    Ellis, T., Makris, D., Black, J.: Learning a Multi-Camera Topology. In: Joint IEEE Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), pp. 165–171 (2003)Google Scholar
  10. 10.
    Black, J., Ellis, T., Makris, D.: Wide Area Surveillance with a Multi-Camera Network. In: Proc. IDSS-04 Intelligent Distributed Surveillance Systems, pp. 21–25 (2003)Google Scholar
  11. 11.
    KaewTrakulPong, P., Bowden, R.: Towards Automated Wide Area Visual Surveillance: Tracking Objects Between Spatially Separated, Uncalibrated Views. Proc. Vision, Image and Signal Processing 152(02), 213–224 (2005)CrossRefGoogle Scholar
  12. 12.
    Sturges, J., Whitfield, T.: Locating Basic Colour in the Munsell Space. Color Research and Application 20(6), 364–376 (1995)CrossRefGoogle Scholar
  13. 13.
    Gretagmacbeth Color Management Solutions,
  14. 14.
    Ilie, A., Welch, G.: Ensuring Color Consistency across Multiple Cameras. Techincal Report TR05-011 (2005)Google Scholar
  15. 15.
    Porikli, F.: Inter-Camera Color Calibration by Cross-Correlation Model Function. In: IEEE International Conference on Image Processing (ICIP), vol. 2, pp. 133–136 (2003)Google Scholar
  16. 16.
    Joshi, N.: Color Calibrator for Arrays of Inexpensive Image Sensors. MS Thesis, Stanford University Department of Computer Science (2004)Google Scholar
  17. 17.
    Stauffer, C., Grimson, W.: Learning Patterns of Activity using Real-time Tracking. PAMI 22(8), 747–757 (2000)CrossRefGoogle Scholar
  18. 18.
    Bowden, R., Gilbert, A., KaewTraKulPong, P.: Tracking Objects Across Uncalibrated Arbitrary Topology Camera Networks. In: S. (ed.) Intelligent Distributed Video Surveillance Systems, ch. 6. IEE, London (2005) (to be published)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andrew Gilbert
    • 1
  • Richard Bowden
    • 1
  1. 1.CVSSPUniversity of SurreyGuildfordEngland

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