ThesisAuthors: Marks, Tim Kalman (2006)
We present a generative graphical model and stochastic filtering algorithm for simulta¬neous tracking of 3D rigid and nonrigid motion, object texture, and background texture from single-camera video. The inference procedure takes advantage of the conditionally Gaussian nature of the model using Rao-Blackwellized particle filtering, which involves Monte Carlo sampling of the nonlinear component of the process and exact filtering of the linear Gaussian component. The smoothness of image sequences in time and space is exploited using Gauss-Newton optimization and Laplace’s method to generate proposal distributions for importance sampling.
Our system encompasses an entire continuum from optic flow to template-based tracking, elucidating the conditions under which each method is optimal... 
