HFGCN:Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition

• URL: http://arxiv.org/abs/2501.11007v3
• Date: Mon, 03 Feb 2025 03:27:51 GMT
• Title: HFGCN:Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition
• Authors: Pengcheng Dong, Wenbo Wan, Huaxiang Zhang, Shuai Li, Sujuan Hou, Jiande Sun,
• Abstract summary: We propose a topological relation classification based on body parts and distance from core of body. In particular, the proposed model is able to focus on the human skeleton points and the different body parts simultaneously. We use a hypergraph to represent the categorical relationships of these skeleton points and incorporate the hypergraph into a graph convolution network.
• Score: 24.492301843927972
• License:
• Abstract: In recent years, action recognition has received much attention and wide application due to its important role in video understanding. Most of the researches on action recognition methods focused on improving the performance via various deep learning methods rather than the classification of skeleton points. The topological modeling between skeleton points and body parts was seldom considered. Although some studies have used a data-driven approach to classify the topology of the skeleton point, the nature of the skeleton point in terms of kinematics has not been taken into consideration. Therefore, in this paper, we draw on the theory of kinematics to adapt the topological relations of the skeleton point and propose a topological relation classification based on body parts and distance from core of body. To synthesize these topological relations for action recognition, we propose a novel Hypergraph Fusion Graph Convolutional Network (HFGCN). In particular, the proposed model is able to focus on the human skeleton points and the different body parts simultaneously, and thus construct the topology, which improves the recognition accuracy obviously. We use a hypergraph to represent the categorical relationships of these skeleton points and incorporate the hypergraph into a graph convolution network to model the higher-order relationships among the skeleton points and enhance the feature representation of the network. In addition, our proposed hypergraph attention module and hypergraph graph convolution module optimize topology modeling in temporal and channel dimensions, respectively, to further enhance the feature representation of the network. We conducted extensive experiments on three widely used datasets.The results validate that our proposed method can achieve the best performance when compared with the state-of-the-art skeleton-based methods.

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