Estimating mode (walking/running/standing) and phases of human locomotion is important for video understanding. We present a new ˇ±tracking as recognitionˇ± approach. A hierarchical finite state machine constructed from 3D motion capture data serves as a prior motion model. Motion templates are used as the observation model. Robustness is achieved by making inferences in the prior motion model which resolves the short-term ambiguity of the observations that may cause a regular tracking formulation to fail. Experiments show very promising results on some difficult sequences.
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