Monte Carlo simulation techniques for probabilistic tracking

In this paper, two novel approaches to probabilistic tracking using Monte Carlo simulation are presented. The first approach is a 3D shape encoded object tracking algorithm. The measurements are derived using the outputs of shape-encoded filters. The nonlinear state estimation is performed by solving the Zakai equation, and we use the branching particle propagation method for computing the solution. The second approach is an algorithm for simultaneous tracking and verification in video data. The approach is based on posterior density estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. Several applications of both approaches including human head and body tracking, human identification and facial feature based face verification are illustrated by experiments devised to evaluate their performance.