基于跨通道特征增强图卷积网络的骨架行为识别  

Cross-Channel Feature-Enhanced Graph Convolutional Network for Skeleton-Based Action Recognition

作　　者：吴志泽 陈盛 檀明 孙斐 杨静 WU Zhize;CHEN Sheng;TAN Ming;SUN Fei;YANG Jing(School of Artificial Intelligence and Big Data,Hefei University,Hefei 230601;School of Mechanical and Electrical Engineering,Hefei Technology College,Hefei 238010)

机构地区：[1]合肥大学人工智能与大数据学院,合肥230601 [2]合肥职业技术学院机电工程学院,合肥238010

出　　处：《模式识别与人工智能》2024年第8期703-714,共12页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.62406095);安徽省自然科学基金面上项目(No.2308085MF213);安徽省重点研发计划项目(No.2022K07020011);安徽省高校科学研究创新团队项目(No.2022AH010095)资助。

摘　　要：受限于图卷积网络的局部操作模式,传统图卷积网络骨架行为识别方法难以建模远关节点关系和长时间信息,无法捕捉动作间的局部微小变化.因此,文中提出基于跨通道特征增强图卷积网络的骨架行为识别(Cross-Channel Feature-Enhanced Graph Convolutional Network for Skeleton-Based Action Recognition,CFE-GCN),包括双部分分组图卷积模块、跨阶段部分密集连接模块及多尺度时间卷积模块.双部分分组图卷积模块采用分组策略,对人体关节点建模,提取多粒度特征,捕获关节点之间的局部细微差异.跨阶段部分密集连接模块建立节点与前网络层之间的关联,丰富早期信息,捕捉长期运动关节间的潜在关系,学习更全面的上下文特征.多尺度时间卷积模块执行不同感受野的时间卷积,捕捉运动在时间域上的短期依赖关系和长期依赖关系.在3个基准数据集上的实验表明CFE-GCN性能较优.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.

关 键 词：图卷积网络 骨架行为识别 跨通道特征增强 密集连接 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]