Tracking gestures using a probabilistic 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 present an extension to the original growing neural gas network that has probabilistic features and can be applied to preserve the topology of a non-stationary distribution. The network consists of the geometrical position of the nodes, the underline local feature structure of the image, and the distance vector between the modal image and any successive images. Accurate correspondence of the nodes between successive images, is measured through the calculation of the topographic product. The method performs continuously mapping over a distribution that changes over time and works with both smooth and abrupt changes. The method is successfully applied to object modelling and tracking.