@article{ref1,
title="Application of convolutional neural network-based 3D posture estimation in behavioral analysis of construction workers",
journal="China safety science journal (CSSJ)",
year="2019",
author="Xiong, R. and Song, Y. and Wang, Y. and Duan, Y.",
volume="29",
number="7",
pages="64-69",
abstract="In order to facilitate an automated behavioral analysis of construction workers, CNN was applied for 3D human pose estimation on sequential images. Considering the complicated site environment and dynamic operator behaviors, the data set of construction workers' postures was developed and the performance of the proposed algorithm was analyzed from both qualitative and quantitative aspects. Furthermore, the derived 3D postures in the video were used to drive the biomechanical model for more detailed and quantitative analysis of worker behavior. The results show that the proposed 3D pose estimation method is accurate and robust, and that combined with biomechanical model, more detailed analysis and evaluation of workers' behavior can be achieved. © 2019 China Safety Science Journal. All rights reserved.<p /><p>Language: zh</p>",
language="zh",
issn="1003-3033",
doi="10.16265/j.cnki.issn1003-3033.2019.07.011",
url="http://dx.doi.org/10.16265/j.cnki.issn1003-3033.2019.07.011"
}