Robust tracking of stochastic deformable models in long image sequences

We describe a method for the temporal tracking of stochastic deformable models in long image sequences. The object representation relies on a hierarchical statistical description of the deformations applied to a template. A Bayesian estimate of the deformations is obtained by maximizing a highly non-linear joint probability distribution. Time consuming global (stochastic) optimization techniques are necessary to obtain optimal solutions unless a good initial guess is available. A good initialization is provided by a recursive temporal filtering of the parameters of the deformable template, combined with a detection of abrupt changes. This procedure yields robust segmentations and enables to track reliably complex deformable structures as is demonstrated here on real-world image sequences showing hand and mouth movements