Self-Organizing Maps for the Automatic Interpretation of Crowd Dynamics

  • B. Zhan
  • P. Remagnino
  • N. Monekosso
  • S. A. Velastin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5358)


This paper introduces the use of self-organizing maps for the visualization of crowd dynamics and to learn models of the dominant motions of crowds in complex scenes. The self-organizing map (SOM) model is a well known dimensionality reduction method proved to bear resemblance with characteristics of the human brain, representing sensory input by topologically ordered computational maps. This paper proposes algorithms to learn and compare crowd dynamics with the SOM model. Different information is employed as input to the used SOM. Qualitative and quantitative results are presented in the paper.


Video Sequence Input Space Dimensionality Reduction Method Crowd Behavior Winning Neuron 
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.


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  1. 1.
    Legion: (Legion group plc),
  2. 2.
    Venegas, S., Knebel, S., Thiran, J.: Multi-object tracking using particle filter algorithm on the top-view plan. Technical report, LTS-REPORT-2004-003, EPFL (2004),
  3. 3.
    Cai, Y., de Freitas, N., Little, J.J.: Robust visual tracking for multiple targets. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 107–118. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Karlsson, R., Gustafsson, F.: Monte Carlo data association for multiple target tracking. Target Tracking: Algorithms and Applications (Ref. No. 2001/174). IEE 1 (2001)Google Scholar
  5. 5.
    Zhan, B., Remagnino, P., Velastin, S., Bremond, F., Thonnat, M.: Matching gradient descriptors with topological constraints to characterise the crowd dynamics. In: IET International Conference on Visual Information Engineering, VIE 2006, pp. 441–446 (2006) ISSN: 0537-9989, ISBN: 978-0-86341-671-2Google Scholar
  6. 6.
    Zhan, B., Remagnino, P., Velastin, S.A., Monekosso, N., Xu, L.Q.: Motion estimation with edge continuity constraint for crowd scene analysis. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Nefian, A., Meenakshisundaram, G., Pascucci, V., Zara, J., Molineros, J., Theisel, H., Malzbender, T. (eds.) ISVC 2006. LNCS, vol. 4292, pp. 861–869. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Andrade, E., Fisher, R.: Modelling crowd scenes for event detection. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR 2006), vol. 01, pp. 175–178. IEEE Computer Society, Washington (2006)CrossRefGoogle Scholar
  8. 8.
    Andrade, E., Fisher, R.: Hidden Markov models for optical flow analysis in crowds. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR 2006), vol. 01, pp. 460–463. IEEE Computer Society, Washington (2006)CrossRefGoogle Scholar
  9. 9.
    Andrade, E.L., Blunsden, S., Fisher, R.B.: Performance analysis of event detection models in crowded scenes. In: Proc. Workshop on Towards Robust Visual Surveillance Techniques and Systems at Visual Information Engineering 2006, Bangalore, India, pp. 427–432 (2006)Google Scholar
  10. 10.
    Zhan, B., Remagnino, P., Velastin, S.: Analysing Crowd Intelligence. In: Second AIxIA Workshop on Ambient Intelligence (2005)Google Scholar
  11. 11.
    Zhan, B., Remagnino, P., Velastin, S.: Visual analysis of crowded pedestrain scenes. In: XLIII Congresso Annuale AICA, pp. 549–555 (2005)Google Scholar
  12. 12.
    Zhan, B., Remagnino, P., Velastin, S.: Mining paths of complex crowd scenes. In: Bebis, G., Boyle, R., Koracin, D., Parvin, B. (eds.) ISVC 2005. LNCS, vol. 3804, pp. 126–133. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Kirt, T., Vainik, E., Võhandu, L.: A method for comparing self-organizing maps: case studies of banking and linguistic data. In: Eleventh East-European Conference on Advances in Databases and Information Systems ADBIS, Varna, Bulgaria, Technical University of Varna, pp. 107–115 (2007)Google Scholar
  14. 14.
    Lefebvre, G., Laurent, C., Ros, J., Garcia, C.: Supervised Image Classification by SOM Activity Map Comparison. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR 2006), vol. 02, pp. 728–731 (2006)Google Scholar
  15. 15.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River (1994)zbMATHGoogle Scholar
  16. 16.
    Polani, D.: Measures for the organization of self-organizing maps. Self-Organizing neural networks: recent advances and applications, 13–44 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • B. Zhan
    • 1
  • P. Remagnino
    • 1
  • N. Monekosso
    • 1
  • S. A. Velastin
    • 1
  1. 1.Digital Imaging Research Centre, Faculty of Computing, Information Systems and MathematicsKingston UniversityUK

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