In this work we introduce a new model-based approach towards the 3-D tracking and extraction of gait patterns in human motion. We suggest the use of a hierarchical, structural model of the human body with a novel derivation of system dynamics from hard and soft kinematic constraints. The hard constraints place physical limitations on possible model configurations while the soft constraints represent probabilistic distributions learned from previous examples of human motion. Using the parameters of the structural and dynamic models, we derive a methodology for extracting a number of gait variables at both coarse and fine resolutions with coincident robustness and precision. In particular, we demonstrate an ability to accurately measure gait velocity, stance width, stride length, arm swing, cadence, and stance times from multi-view, video sequences of human movement captured in a complex home environment.