Active-GNG: model acquisition and tracking in cluttered backgrounds

Authors:
Anastassia Angelopoulou;Alexandra Psarrou;José Garcia Rodriguez;Gaurav Gupta
Affiliations:
University of Westminster, Harrow, London, United Kingdom;University of Westminster, Harrow, London, United Kingdom;University of Alicante, Alicante, Spain;University of Westminster, Harrow, London, United Kingdom
Venue:
VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
Year:
2008

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Abstract

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 space. Traditionally these models neither do include local adaptation of the nodes nor colour information. In this paper, we extend GNG by adding an active step to the network, which we call Active-Growing Neural Gas (A-GNG) that has both global and local properties and can track in cluttered backgrounds. The approach is novel in that the topological relations of the model are based on a number of attributes (e.g. global and local transformations, mapping function and skin colour information) which allow us to automatically model and track 2D gestures. To measure the quality of the tracked correspondences we use two interlinked topology preservation measures. Experimental results have shown better performance of our proposed method over the original GNG and the Active Contour Model.