Building visual surveillance systems with neural networks.

Garcia Rodriguez, Jose, Angelopoulou, Anastassia, Mora-Gimeno, F.J. and Psarrou, Alexandra (2012) Building visual surveillance systems with neural networks. In: Computational Intelligence for Privacy and Security. Studies in Computational Intelligence, 394 . Springer, pp. 181-198. ISBN 9783642252365

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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.

Item Type: Book Section
Subjects: University of Westminster > Science and Technology > Electronics and Computer Science, School of (No longer in use)
Depositing User: Rachel Wheelhouse
Date Deposited: 24 Apr 2012 09:25
Last Modified: 24 Apr 2012 09:25

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