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We present an approach to track human subjects using an articulated human framework. First, we describe the articulated hierarchical human model. Second, we develop a stochastic hierarchical, partitioned, particle filter based on the natural structure and limb dependency of the human body. We apply this to track human subjects in video sequences using likelihoods adapted to the hierarchical process. Finally, we evaluate the effectiveness of the described approach using publicly available datasets.