Statistical model-based estimation and tracking of non-rigid motion
We describe a method for the temporal tracking of stochastic deformable models in image sequences. The object representation relies on a hierarchical statistical description of the deformations applied to a template. The optimal Bayesian estimate of deformations is obtained by maximizing nonlinear probability distributions using optimization techniques. The method may be sensitive to local maxima of the distributions and require an initial configuration close to the optimal solution. In our approach, the initialization is provided by a robust estimate of the rigid and statistically constrained nonrigid motions from the normal optical flow computed along the deformable contour. The approach is demonstrated on real-world sequences showing mouth movements and cardiac motions with missing data.