基于跨通道特征增强图卷积网络的骨架行为识别

doi:10.16451/j.cnki.issn1003-6059.202408004

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Abstract Traditional graph convolutional networks for skeleton-based action recognition struggle to model long-range joint relationships and long-term temporal information due to their local operation mode, failing to capture subtle variations between actions. To address this problem, a cross-channel feature-enhanced graph convolutional network(CFE-GCN) for skeleton-based action recognition is proposed including a dual part-wise grouping graph convolution(DPG-GC) module, a cross-stage partial dense connections(CS-PDC) module and a multi-scale temporal convolution(MS-TC) module. The DPG-GC module models the human body joints by a grouping strategy to extract multi-granularity features and capture the subtle local differences between the joints. The CS-PDC module establishes associations between nodes and the previous network layers, enriching the early information and capturing the potential long-term relationships between the moving joints, and thereby contextual features are learned more comprehensively. The MS-TC module performs temporal convolution with different receptive fields to capture both short-term and long-term dependencies in the temporal domain. Experiments show that CFE-GCN achieves superior performance on multiple benchmark datasets.

Key words： Graph Convolutional Network Skeleton-Based Action Recognition Cross-Channel Feature Enhancement Dense Connections

Received: 28 June 2024

ZTFLH:

TP391

Fund:National Natural Science Foundation of China(No.62406095), Natural Science Foundation of Anhui Province(No.2308085MF213), Key Research Plan of Anhui Province(No.2022K07020011), University Scientific Research Innovation Team Project of Anhui Province(No.2022AH010095)

Corresponding Authors: YANG Jing, Ph.D., associate professor. Her research interests include deep learning, image processing and graph neural networks.

About author:: WU Zhize, Ph.D., associate professor. His research interests include deep learning-driven image and video processing and ana-lysis. CHEN Sheng, Master student. His research interests include graph neural network and skeleton based action recognition. TAN Ming, Master, professor. His research interests include pattern recognition and computer vision. SUN Fei, Master, professor. His research interests include pattern recognition and computer vision.

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Cite this article:

WU Zhize,CHEN Sheng,TAN Ming等. Cross-Channel Feature-Enhanced Graph Convolutional Network for Skeleton-Based Action Recognition[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(8): 703-714.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202408004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2024/V37/I8/703

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