Human Action Recognition Using BI-LSTM Network Based on Spatial Features

With the advent of depth cameras such as microsoft Kinect, the method of recognizing human action via 3D skeleton node data with simplicity, robustness and view-independent representation has achieved quite good performance. However, most of the existing methods for skeleton sequence data lack spatial structure information and detailed temporal dynamics features. By means of the characteristics of BI-LSTM model with the long-term storage of skeleton sequences, rich bidirectional time information to model the sequence of actions is obtained. Meanwhile, the relative distance features and relative angle features between joint points are extracted from 3D bone joint point coordinates to strengthen the spatial structure features and realize the recognition of human action from the skeleton data. Finally, it is shown via the simulation results that this proposed method can effectively achieve the classification of human actions and improve the accuracy.