计算机应用与软件2024，Vol.41Issue(4)：151-158,8.DOI:10.3969/j.issn.1000-386x.2024.04.023
基于改进时空图卷积网络的人员交互行为识别
HUMAN INTERACTION BEHAVIOR RECOGNITION BASED ON IMPROVED SPATIAL TEMPROAL GRAPH CONVOLUTION NETWORK
摘要
Abstract
Aimed at the problems that the recognition accuracy and model performance cannot be satisfied by multi-modal data fusion method for human interaction behavior recognition,a human interaction behavior recognition method based on improved spatial temporal graph convolutional network is proposed.The single-modal skeleton data was introduced into the cascaded densely spatial temporal graph convolutional block network to obtain rich spatial-temporal feature information and improve the feature reuse rate.An enhanced spatial temporal convolution network(EST-GCN)unit was designed to improve the information representation ability of the network between joints.A motion characteristic factor was introduced to measure the importance of different joints in the limbs to improve the model recognition effect.The experimental results on the Kinetics dataset and the case-handling area scene dataset show that the proposed method has certain advantages in the recognition effect,and the method is very competitive in model complexity and operating ef-ficiency.关键词
交互行为/时空图卷积网络/骨架数据/密集Key words
Interactive behavior/Spatial temporal graph convolution network/Skeleton data/Densely分类
信息技术与安全科学引用本文复制引用
雷静思,刘双广,刘乔寿,王祥雪..基于改进时空图卷积网络的人员交互行为识别[J].计算机应用与软件,2024,41(4):151-158,8.基金项目
国家自然科学基金项目(61901071) （61901071）
重庆市自然科学基金重点项目(cstc2020jcyj-zdxmX0024). （cstc2020jcyj-zdxmX0024）
