Continuous human activity recognition

Effectively recognizing human activities requires at least 32 joint related degrees of freedom to be estimated so as to reliably track the human body in 3D. The particle filter is robust to distracting clutter by maintaining multiple hypotheses for each of these joint angles. Real-time tracking is difficult however with the computational overhead of such a large search space. This paper optimizes this search space utilizing feedback from a continuous human activity recognition (CHAR) system and improves the robustness and efficiency of each particle calculation using a novel body model. The joint angles are estimated for the next frame using a particle filter with forward smoothing. A new paradigm enables the temporal segmentation of continuous motion into dynemes. Using HMM, the CHAR system attempts to infer the human movement skill that could have produced the observed sequence of dynemes. Hundreds of movement skills, from gait to saltos, are successfully tracked and recognized.

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