Abstract

3D skeletal data has recently attracted wide attention in human behavior analysis for its robustness to variant scenes, while accurate gesture recognition is still challenging. The main reason lies in the high intra-class variance caused by temporal dynamics. A solution is resorting to the generative models, such as the hidden Markov model (HMM). However, existing methods commonly assume fixed anchors for each hidden state, which is hard to depict the explicit temporal structure of gestures. Based on the observation that a gesture is a time series with distinctly defined phases, we propose a new formulation to build temporal compositions of gestures by the low-rank matrix decomposition. The only assumption is that the gesture’s “hold” phases with static poses are linearly correlated among each other. As such, a gesture sequence could be segmented into temporal states with semantically meaningful and discriminative concepts. Furthermore, different to traditional HMMs which tend to use specific distance metric for clustering and ignore the temporal contextual information when estimating the emission probability, the Long Short-Term Memory (LSTM) is utilized to learn probability distributions over states of HMM. The proposed method is validated on two challenging datasets. Experiments demonstrate that our approach can effectively work on a wide range of gestures and actions, and achieve state-of-the-art performance.