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We introduce a new approach to motion-based recognition that combines the temporally descriptive abilities of a hidden Markov model (HMM) with the inferential power of a Bayesian belief network. We define activities using a collection of multiple Markov models, each associated with a unique set of body model parameters or gait variables. A single Bayesian network integrates the models by operating on virtual evidence derived from the HMM conditional output probabilities. We introduce both fundamental and auxiliary models for characterizing events and tracking failures, respectively. We demonstrate the system using multi-view video sequences corrupted by occlusion, noise, and entirely missing observations.