首页|Construction Activity Analysis of Workers Based on Human Posture Estimation Information
Construction Activity Analysis of Workers Based on Human Posture Estimation Information
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
Identifying workers'construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress.However,current activity analysis methods for construction work-ers rely solely on manual observations and recordings,which consumes considerable time and has high labor costs.Researchers have focused on monitoring on-site construction activities of workers.However,when multiple workers are working together,current research cannot accurately and automatically iden-tify the construction activity.This research proposes a deep learning framework for the automated anal-ysis of the construction activities of multiple workers.In this framework,multiple deep neural network models are designed and used to complete worker key point extraction,worker tracking,and worker con-struction activity analysis.The designed framework was tested at an actual construction site,and activity recognition for multiple workers was performed,indicating the feasibility of the framework for the auto-mated monitoring of work efficiency.
Pose estimationActivity analysisObject trackingConstruction workersAutomatic systems
Xuhong Zhou、Shuai Li、Jiepeng Liu、Zhou Wu、Yohchia Frank Chen
展开 >
School of Civil Engineering,Chongqing University,Chongqing 400044,China
School of Automation,Chongqing University,Chongqing 400044,China
Department of Civil Engineering,The Pennsylvania State University,Middletown,PA 17057,USA
NETL
NSTL
万方数据
