基于CNN的3D姿势估计在建筑工人行为分析中的应用

doi:10.16265/j.cnki.issn1003-3033.2019.07.011

中国安全科学学报 ›› 2019, Vol. 29 ›› Issue (7): 64-69.doi: 10.16265/j.cnki.issn1003-3033.2019.07.011

基于CNN的3D姿势估计在建筑工人行为分析中的应用

熊若鑫¹, 宋元斌¹ 高级工程师, 王宇轩², 段彦娟³

1 上海交通大学船舶海洋与建筑工程学院,上海 200240;
2 东南大学交通学院,江苏南京 211189;
3 上海中医药大学附属第七人民医院皮肤科,上海 200137

收稿日期:2019-04-25 修回日期:2019-05-29 出版日期:2019-07-28 发布日期:2020-10-21
作者简介:熊若鑫 (1994—),男,湖北黄冈人,硕士研究生,主要研究方向为机器视觉、行为安全。E-mail: xiongrxchn@sjtu.edu.cn。
基金资助:
国家自然科学基金资助(71271137);浦东新区中医药研发与创新专项(PDZYYFCX-201804)。

Application of convolutional neural network-based 3D posture estimation in behavioral analysis of construction workers

XIONG Ruoxin¹, SONG Yuanbin¹, WANG Yuxuan², DUAN Yanjuan³

1 School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China;
2 School of Transportation, Southeast University, Nanjing Jiangsu 211189, China;
3 Department of Dermatology, Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai 200137, China

Received:2019-04-25 Revised:2019-05-29 Online:2019-07-28 Published:2020-10-21

摘要/Abstract

摘要： 为实现建筑工人现场行为的自动化分析,采用卷积神经网络(CNN)检测3D人体姿势并根据现场条件对连续图像进行姿态估计;考虑到动态和杂乱的施工现场环境(部分遮挡等)及多变的工人行为,开发建筑工人姿势图像数据集,从定性和定量2方面综合测试算法性能;将所提出的方法用于施工作业姿势风险评估,利用视频中工人的3D姿势驱动人体生物力学模型,快速、定量计算工人作业时易损伤的部位。结果表明:该人体姿势估计方法具有较好的鲁棒性和较高的准确性,结合生物力学模型可实现更精细的工人行为分析与评估。

关键词: 3D姿势估计, 卷积神经网络(CNN), 行为分析, 现场测试, 施工安全

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.

Key words: 3D posture estimation, convolutional neural network (CNN), behavioral analysis, field test, construction safety

中图分类号:

X912.9

熊若鑫, 宋元斌, 王宇轩, 段彦娟. 基于CNN的3D姿势估计在建筑工人行为分析中的应用[J]. 中国安全科学学报, 2019, 29(7): 64-69.

XIONG Ruoxin, SONG Yuanbin, WANG Yuxuan, DUAN Yanjuan. Application of convolutional neural network-based 3D posture estimation in behavioral analysis of construction workers[J]. China Safety Science Journal, 2019, 29(7): 64-69.

参考文献

[1] 郭红领, 刘文平, 张伟胜. 集成BIM和PT的工人不安全行为预警系统研究[J]. 中国安全科学学报, 2014, 24(4): 104-109.
GUO Hongling, LIU Wenping, ZHANG Weisheng. A BIM-PT-integrated warning system for on-site workers' unsafe behavior [J]. China Safety Science Journal, 2014, 24(4): 104-109.
[2] PEDDI A, HUAN L, BAI Yong, et al. Development of human pose analyzing algorithms for the determination of construction productivity in real-time[C]. Construction Research Congress, 2009: 11-20.
[3] 晋良海, 张荣坤, 郑霞忠,等. 隧洞钻孔作业工效及低位作业姿势研究[J]. 中国安全科学学报, 2016, 26(4): 66-71.
JIN Lianghai, ZHANG Rongkun, ZHENG Xiazhong, et al. Research on simulation of drilling work posture and optimization of lowing work posture for tunnel [J]. China Safety Science Journal, 2016, 26(4): 66-71.
[4] 佟瑞鹏, 陈策, 刘思路,等. 面向行为安全的泛场景数据理论与应用研究[J]. 中国安全科学学报, 2017, 27(2): 1-6.
TONG Ruipeng, CHEN Ce, LIU Silu, et al. Research on theory and application of pan-scene data for behavioral safety [J]. China Safety Science Journal, 2017, 27(2): 1-6.
[5] 陈大伟, 田翰之, 张江石. 基于行为安全的建筑事故预防量化方法与实证研究[J]. 中国安全科学学报, 2010, 20(7): 96-102.
CHEN Dawei, TIAN Hanzhi, ZHANG Jiangshi. A quantitative method for construction accidents prevention based on behavior safety and its empirical study [J]. China Safety Science Journal, 2010, 20(4): 96-102.
[6] 韩豫, 张泾杰, 孙昊,等. 基于图像识别的建筑工人智能安全检查系统设计与实现[J]. 中国安全生产科学技术, 2016, 12(10): 142-148.
HAN Yu, ZHANG Jingjie, SUN Hao, et al. Design and implementation of intelligent safety inspection system for construction workers based on image recognition [J]. Journal of Safety Science and Technology, 2016, 12(10): 142-148.
[7] TOSHEV A, SZEGEDY C. DeepPose: human pose estimation via deep neural networks [C]. IEEE Conference on Computer Vision and Pattern Recognition, 2014: 1 653-1 660.
[8] WEI S, RAMAKRISHNA V, KANADE T, et al. Convolutional Pose Machines [C]. IEEE Conference on Computer Vision and Pattern Recognition, 2016: 4 724-4 732.
[9] NEWELL A, YANG Kaiyu, DENG Jiang. Stacked hourglass networks for human pose estimation [C]. European Conference on Computer Vision, 2016: 483-499.
[10] 熊若鑫, 宋元斌, 王宇轩. 基于DNN的作业姿势评估方法及应用[J]. 中国安全科学学报, 2018, 28(5): 105-110.
XIONG Ruoxin, SONG Yuanbin, WANG Yuxuan. Deep neural network-based posture assessment method and its application [J]. China Safety Science Journal, 2018, 28(5): 105-110.
[11] IQBAL U, DOERING A, YASIN H, et al. A dual-source approach for 3d human pose estimation from single images [C]. IEEE Conference on Computer Vision and Pattern Recognition, 2016: 4 948-4 956.
[12] 尹燕芳, 孙农亮, 刘明,等. 基于BSCPs-RF的人体关节点行为识别与预测[J]. 机器人, 2017, 39(6): 795-802.
YIN Yanfang, SUN Nongliang, LIU Ming, et al. Action recognition and prediction of human skeleton based on BSCPs-RF [J]. Robot, 2017, 39(6): 795-802.
[13] SAPP B, TASKAR B. MODEC: multimodal decomposable models for human pose estimation [C]. IEEE Conference on Computer Vision and Pattern Recognition, 2013: 3 674-3 681.
[14] JOHNSON S, EVERINGHAM M. Clustered pose and nonlinear appearance models for human pose estimation [C]. Proceedings of the British Machine Vision Conference, 2010: 12.1-12.11.
[15] ANDRILUKA M, PISHCHULIN L, GEHLER P, et al. 2D human pose estimation: new benchmark and state of the art analysis [C]. IEEE Conference on Computer Vision and Pattern Recognition, 2014: 3 686-3 693.
[16] IONESCU C, PAPAVA D, OLARU V, et al. Human3.6M: large scale datasets and predictive methods for 3d human sensing in natural environments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7): 1 325-1 339.
[17] JOO H, LIU Hao, TAN Lei, et al. Panoptic studio: a massively multiview system for social motion capture [C]. IEEE International Conference on Computer Vision, 2015: 3 334-3 342.
[18] HIGNETT S, MCATAMNEY L. Rapid entire body assessment (REBA) [J]. Applied Ergonomics, 2000, 31(2): 201-205.
[19] 纪仲秋, 李旭龙, 姜桂萍,等. AnyBody仿真和验证的太极拳训练中下肢生物力学特征[J]. 中国康复医学杂志, 2014, 29(9): 799-805.
JI Zhongqiu, LI Xulong, JIANG Guiping, et al. A biomechanical research of lower limbs in taichiquan with simulation and modeling system [J]. Chinese Journal of Rehabilitation Medicine, 2014, 29(9): 799-805.
[20] National Institute for Occupational Safety. Work practices guide for manual lifting [R], 1981.

基于CNN的3D姿势估计在建筑工人行为分析中的应用

Application of convolutional neural network-based 3D posture estimation in behavioral analysis of construction workers

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