Methods, devices, and non-transitory computer readable storage medium are provided for recognizing a human action using a graph convolutional network (GCN). The method includes obtaining, by a device, a plurality of joint poses (410). The device includes a memory storing instructions and a processor in communication with the memory. The method also includes normalizing, by the device, the plurality of joint poses to obtained a plurality of normalized joint poses (420); extracting, by the device, a plurality of rough features using a modified spatial-temporal GCN (ST-GCN) from the plurality of normalized joint poses (430); reducing, by the device, a feature dimension of the plurality of rough features to obtain a plurality of dimension-shrunk features (440); refining, by the device, the plurality of dimension-shrunk features based on a self-attention model to obtain a plurality of refined features (450); and recognizing, by the device, a human action based on the plurality of refined features (460).
ACTIONAL-STRUCTURAL SELF-ATTENTION GRAPH CONVOLUTIONAL NETWORK FOR ACTION RECOGNITION