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The difficulties introduced by large degrees of freedom are still a challenge in articulated human body tracking. In this paper, an efficient tracker is proposed based on the integration of a set of statistical techniques including KLD sampling, Rao-Blackwellisation, and Particle filtering. This results in a KLD-Annealed Particle filter with Rao-Blackwellisation, which can address the key issues in 3D human tracking, such as accuracy, stability, and speed simultaneously. Both synthetic and real data were used in our experiments to demonstrate the improved performance of the proposed tracker.