Tracking human motion from monocular video sequences has attracted a great deal of interests in recent years. The difficulty in solving this problem is largely due to the nonlinear property of human dynamics and the high dimensionality of the state vector space required to model human motion. Traditional particle filtering methods usually fail in this situation because the distributions they sample from are ill-defined. In this thesis we propose a novel tracking algorithm, namely the Differential Evolution - Markov Chain (DE-MC) particle filtering. It is based on the particle filter framework but makes substantial changes to its core, i.e. the sampling strategy. In this new approach, the Differential Evolution algorithm and the Markov Chain Monte Carlo algorithm are integrated, aiming at improving both the accuracy and efficiency in approximating the posterior distribution. Global optimization and importance sampling are spirits of the proposed method. To apply the DE-MC particle filter to articulated model-based human motion tracking, we also integrate multiple image cues including the area of silhouettes, color histograms and boundaries to measure the image likelihoods. We find the Fourier Descriptor (FD) to be a new and effective image feature in human motion tracking applications. Our other contributions, such as a modified color cue-based measurement function and a simple adaptive strategy for sampling, also help to improve the performance of the human tracker. Experimental results including the comparison with the performance of other particle filtering methods demonstrate the power of the proposed approach.