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Human 3D Motion Recognition Based on Spatial-Temporal Context of Joints

The paper presents a novel human motion recognition method based on a new form of the Hidden Markov Models, called spatial-temporal hidden markov models (ST-HMM), which can be learnt from a sequence of joints positions. To cope with the high dimensionality of the pose space, in this paper, we exploit the spatial dependency between each pair of spatially connected joints in the articulated skeletal structure, as well as the temporal dependency due to the continuous movement of each of the joints. The spatial-temporal contexts of these joints are learnt from the sequences of joints movements and captured by our ST-HMM. Results of recognizing 11 different action classes on a large number of motion capture sequences as well as synthetic tracking data show that our approach outperforms traditional HMM approach in terms of robustness and recognition rates.