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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)

Abstract

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.

Keywords

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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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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