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Realtime Kernel based Tracking

We present a solution for realtime tracking of a planar pattern. Tracking is seen as the estimation of a parametric function between observations and motion and we propose an extension of the learning based approach presented simultaneously by Cootes and al. and by Jurie and Dhome. We show that the hyperplane classic algorithm is a specific case of a more generic linearly-weighted sum of fixed non-linear basis functions model. The weights associated to the basis functions (kernel functions) of the model are estimated from a training set of perturbations and associated observations generared in a synthetic way. The resulting tracker is then composed by several iterations on trackers learned with coarse to fine magnitude of perturbations. We compare the performance of the method with the linear algorithm in terms of accuracy and convergence frequency. Moreover, we illustrate the behaviour of the method for several real toy video sequences including different patterns, motions and illumination conditions, and for several real video sequences sampling from rear car tracking databases.