Visual surveillance of objects motion using GNG

Abstract

Self-organising neural networks preserves the topology of an input space by using their competitive learning. In this work we use a kind of self-organising network, the Growing Neural Gas, 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 used to build a system able to track image features in video image sequences. The system automatically keeps correspondence of features among frames in the sequence using its own structure. © 2009 Springer Berlin Heidelberg.