西安工程大学学报2025，Vol.39Issue(2)：1-9,9.DOI:10.13338/j.issn.1674-649x.2025.02.001
基于卷积注意力模块的人体姿态估计研究
Research on human pose estimation based on convolutional block attention module
摘要
Abstract
In order to further ensure pedestrian safety in autonomous driving,this article addres-ses the issues of key point false detection,missed detection,and redundancy in human pose esti-mation.HRNet was used as the backbone network for algorithm optimization to further improve model detection accuracy.Firstly,a human pose estimation model inference network RSGNet was designed for image feature extraction,which eliminates the influence of interfering keypoints dur-ing the keypoint inference process and improves the effective utilization of keypoint information by the model.Secondly,in response to the problem of incomplete image detail information caused by self occlusion or external interference,a convolutional block attention module(CBAM)was added to image feature processing.This module combines spatial and channel correlation fu-sion information to reduce the negative impact of foreground,background,and other information on image processing.The experimental results show that compared with the benchmark model HRNet method,the improved network model significantly improves the detection accuracy of hu-man pose estimation,with an average precision(AP)increase of 7.3%in the public dataset CO-CO,and the AP in the public data set MPII is increased by 3.0%.关键词
姿态估计/自动驾驶/关键点/注意力机制/空间注意力/通道注意力Key words
pose estimation/autonomous driving/key points/attention mechanisms/spatial atten-tion/channel attention分类
信息技术与安全科学引用本文复制引用
廉继红,薛维哥,王延年,张楠..基于卷积注意力模块的人体姿态估计研究[J].西安工程大学学报,2025,39(2):1-9,9.基金项目
陕西省科技厅一般项目(2022GY-053) （2022GY-053）
