García Rodríguez, José and Angelopoulou, Anastassia and Garci?a-Chamizo, Juan Manuel and Psarrou, Alexandra (2010) GNG based surveillance system. In: 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, 18-23 July 2010. IEEE, pp. 1-8. ISBN 9781424469161Full text not available from this repository.
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. It has been used their capacity to represent non rigid objects as a result of an adaptive process by a topology-preserving graph that constitutes an induced Delaunay triangulation of their shapes. 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:||Miss Nina Watts|
|Date Deposited:||15 Sep 2011 12:00|
|Last Modified:||15 Sep 2011 12:00|
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