Tracking human movement patterns using particle filtering

At least 32 joint related degrees of freedom need to be estimated 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 movement recognition (CHMR) 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 CHMR 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.