Hand gesture modelling and tracking using a Self-Organising Network

The Self-Organising Artificial Neural Network Models, of which we have used the Growing Neural Gas (GNG) can be applied to preserve the topology of an input distribution. Traditionally these models neither do include local adaptation of the nodes nor colour information. In this paper, we extend GNG by presenting an improvement to the network that has both global and local properties and can track in cluttered backgrounds. The method performs continuously mapping over a distribution that changes over time and works with both smooth and abrupt changes. The central mechanism relies on the addition of global and local attributes, and skin colour information to the network which allow us to automatically model and track 2D gestures. Application to hand gesture video tracking is presented.