计算机与数字工程2025，Vol.53Issue(4)：1008-1014,7.DOI:10.3969/j.issn.1672-9722.2025.04.016
基于特征增强与重参数的骨骼动作识别
Skeleton-based Action Recognition Based on Feature Enhancement and Reparameterization
摘要
Abstract
With the development of deep learning,the model of skeleton-based action recognition based on deep learning is becoming more and more complex,the network level is getting deeper and deeper,and the model weight is getting larger and larg-er,so there is a disadvantage of slow inference speed,so this paper proposes a skeleton-based action recognition(RepGCN)based on feature enhancement and reparameterization method.In order to improve the performance of the model,a feature enhancement method combining bone joint information,bone information,and bone angle information is proposed.Then,an adaptive map based on multi-scale is proposed for the input of graph convolution,which guides the graph convolution to extract deeper action features to improve the recognition performance of the model,and finally this paper proposes a way to train and test understanding coupling to simplify the complexity of the model and improve the inference speed of the model.It can reduce the amount of model parameters,facilitate the deployment of models and the application of mobile terminals.关键词
图卷积神经网络/重参数化/人体骨骼动作识别Key words
graph convolutional neural network/reparameterization/human skeletal action recognition分类
信息技术与安全科学引用本文复制引用
李豆豆,李汪根,夏义春,葛英奎,王志格..基于特征增强与重参数的骨骼动作识别[J].计算机与数字工程,2025,53(4):1008-1014,7.基金项目
高校领军人才引进与培育计划项目(编号:051619)资助. （编号:051619）
