Fusing angular features for skeleton‐based action recognition using multi‐stream graph convolution network.

Distinguishing similar actions has been a challenging challenge in skeleton‐based action recognition. Since the joint coordinates in these actions are similar, it is difficult to accomplish the recognition task using traditional joint features. To address this issue, the use of angle features to capture subtle nuances in various body parts, along with a critical angle enhancement module that assigns weights to different angle feature representations for a given action are proposed, highlighting the critical angle feature representation. The approach is evaluated using a three‐stream ensemble method on three large action recognition datasets, NTU‐RGB+D, NTU‐RGB+D 120, and Kinetics‐400. The experimental results demonstrate that incorporating angular information can effectively complement joint and skeletal features, leading to improved recognition of similar actions and enhanced model performance and robustness. [ABSTRACT FROM AUTHOR]