Realtime Kernel based Machine Learning Template Matching (KMLT)

Abstract

This paper deals with a new approach for the problemof realtime planar templatematching. We considertracking as the estimation of a parametric function between observations and motion and we propose anextension of the learning based approach presented simultaneously by Cootes and al. and by Jurie andDhome to non linear regression functions. The estimation of the linear parameters associated to the basisfunctions (kernel functions) of the model is then achieved using a training set of motions and associatedobservations. We show that the resulting method outperforms the robustness of the linear tracker againstnoisy observations.