Building Visual Surveillance Systems with Neural Networks

  • J. García-Rodríguez
  • A. Angelopoulou
  • F. J. Mora-Gimeno
  • A. Psarrou
Part of the Studies in Computational Intelligence book series (SCI, volume 394)


Self-organising neural networks have shown promise in a variety of applications areas. Their massive and intrinsic parallelism makes those networks suitable to solve hard problems in image-analysis and computer vision applications, especially when non-stationary environments occur. Moreover, this kind of neural networks preserves the topology of an input space by using their inherited competitive learning property. In this work we use a kind of self-organising network, the Growing Neural Gas, to solve some computer vision tasks applied to visual surveillance systems. The neural network is also modified to accelerate the learning algorithm in order to support applications with temporal constraints. This feature has been used to build a system able to track image features in video sequences. The system automatically keeps the correspondence of features among frames in the sequence using its own structure. Information obtained during the tracking process and allocated in the neural network can also be used to analyse the objects motion.


Input Space Previous Frame Reference Vector Hand Gesture Recognition Visual Surveillance 
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 2012

Authors and Affiliations

  • J. García-Rodríguez
    • 1
  • A. Angelopoulou
    • 2
  • F. J. Mora-Gimeno
    • 1
  • A. Psarrou
    • 2
  1. 1.Dept. of Computer TechnologyUniversity of AlicanteAlicanteSpain
  2. 2.Dept. of Computer Science and Software EngineeringUniversity of WestminsterLondonUK

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