This paper presents the first systematic empirical study of the particle filter (PF) algorithms for human figure tracking in video. Our analysis and evaluation follows a modular approach which is based upon the underlying statistical principles and computational concerns that govern the performance of PF algorithms. Based on our analysis, we propose a novel PF algorithm for figure tracking with superior performance called the Optimized Unscented PF. We examine the role of edge and template features, introduce computationally-equivalent sample sets, and describe a method for the automatic acquisition of reference data using standard motion capture hardware. The software and test data are made publicly-available on our project website.