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Skeleton-based action recognition is a typical classification problem which plays a significant role in human-computer interaction and video understanding. Since a human skeleton has natural graphic features, methods based on graph convolutional networks (GCN) are widely applied in skeleton-based action recognition. Previous studies mainly focus on structural links in GCN to generate high-level features of human skeleton. However, low-level features are also important in many applications. For instance, low-level edge gradient and color information are important for image classification. This paper introduces a multi-branches structure to capture different low-level features of human skeleton. We combine both high-level and low-level features to recognize human action. We validate our method in action recognition with two skeleton datasets, NTU-RGB&#x002B;D and Kinetics. Experiment results indicate that the proposed method achieves considerable improvement over some state-of-the-art methods.

Skeleton-Based Action Recognition With Low-Level Features of Adaptive Graph Convolutional Networks