Nonparametric modelling and tracking with active-GNG

Authors:
Anastassia Angelopoulou;Alexandra Psarrou;Gaurav Gupta;José García-Rodríguez
Affiliations:
Harrow School of Computer Science, University of Westminster, Harrow, United Kingdom;Harrow School of Computer Science, University of Westminster, Harrow, United Kingdom;Harrow School of Computer Science, University of Westminster, Harrow, United Kingdom;Department of Computer Technology and Computation, University of Alicante, Alicante, Spain
Venue:
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Year:
2007

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Abstract

In this paper we address the correspondence problem, with its application to nonrigid tracking and unsupervised modelling, as a nonparametric, active-linking topology learning problem. Unlike existing soft competitive learning methods, Active Growing Neural Gas (A-GNG) has both global and local properties which allows part of the network to reconfigure while tracking. In addition, A-GNG uses a number of features (e.g. topographic product, local grey-level and map transformation) so that the topological relations are preserved and nodes correspondences are retained between tracked configurations. Experimental results in a sequence of hand gestures and artificial data have shown the superiority of our proposed method over the original GNG.