IEICE TRANSACTIONS on Information
Computer Vision-Based Tracking of Workers in Construction Sites Based on MDNet
Summary :
Automatic continuous tracking of objects involved in a construction project is required for such tasks as productivity assessment, unsafe behavior recognition, and progress monitoring. Many computer-vision-based tracking approaches have been investigated and successfully tested on construction sites; however, their practical applications are hindered by the tracking accuracy limited by the dynamic, complex nature of construction sites (i.e. clutter with background, occlusion, varying scale and pose). To achieve better tracking performance, a novel deep-learning-based tracking approach called the Multi-Domain Convolutional Neural Networks (MD-CNN) is proposed and investigated. The proposed approach consists of two key stages: 1) multi-domain representation of learning; and 2) online visual tracking. To evaluate the effectiveness and feasibility of this approach, it is applied to a metro project in Wuhan China, and the results demonstrate good tracking performance in construction scenarios with complex background. The average distance error and F-measure for the MDNet are 7.64 pixels and 67, respectively. The results demonstrate that the proposed approach can be used by site managers to monitor and track workers for hazard prevention in construction sites.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.653-661
- Publication Date
- 2023/05/01
- Publicized
- 2022/10/20
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022DLP0045
- Type of Manuscript
- Special Section PAPER (Special Section on Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications)
- Category
- Smart Industry
Authors
Wen LIU
CCCC Second Harbor Engineering Co., Ltd.
Yixiao SHAO
Huazhong University of Science and Technology
Shihong ZHAI
CCCC Second Harbor Engineering Co., Ltd.
Zhao YANG
CCCC Second Harbor Engineering Co., Ltd.
Peishuai CHEN
CCCC Second Harbor Engineering Co., Ltd.
Keyword
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Wen LIU, Yixiao SHAO, Shihong ZHAI, Zhao YANG, Peishuai CHEN, "Computer Vision-Based Tracking of Workers in Construction Sites Based on MDNet" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 653-661, May 2023, doi: 10.1587/transinf.2022DLP0045.
Abstract: Automatic continuous tracking of objects involved in a construction project is required for such tasks as productivity assessment, unsafe behavior recognition, and progress monitoring. Many computer-vision-based tracking approaches have been investigated and successfully tested on construction sites; however, their practical applications are hindered by the tracking accuracy limited by the dynamic, complex nature of construction sites (i.e. clutter with background, occlusion, varying scale and pose). To achieve better tracking performance, a novel deep-learning-based tracking approach called the Multi-Domain Convolutional Neural Networks (MD-CNN) is proposed and investigated. The proposed approach consists of two key stages: 1) multi-domain representation of learning; and 2) online visual tracking. To evaluate the effectiveness and feasibility of this approach, it is applied to a metro project in Wuhan China, and the results demonstrate good tracking performance in construction scenarios with complex background. The average distance error and F-measure for the MDNet are 7.64 pixels and 67, respectively. The results demonstrate that the proposed approach can be used by site managers to monitor and track workers for hazard prevention in construction sites.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022DLP0045/_p
Copy
@ARTICLE{e106-d_5_653,
author={Wen LIU, Yixiao SHAO, Shihong ZHAI, Zhao YANG, Peishuai CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={Computer Vision-Based Tracking of Workers in Construction Sites Based on MDNet},
year={2023},
volume={E106-D},
number={5},
pages={653-661},
abstract={Automatic continuous tracking of objects involved in a construction project is required for such tasks as productivity assessment, unsafe behavior recognition, and progress monitoring. Many computer-vision-based tracking approaches have been investigated and successfully tested on construction sites; however, their practical applications are hindered by the tracking accuracy limited by the dynamic, complex nature of construction sites (i.e. clutter with background, occlusion, varying scale and pose). To achieve better tracking performance, a novel deep-learning-based tracking approach called the Multi-Domain Convolutional Neural Networks (MD-CNN) is proposed and investigated. The proposed approach consists of two key stages: 1) multi-domain representation of learning; and 2) online visual tracking. To evaluate the effectiveness and feasibility of this approach, it is applied to a metro project in Wuhan China, and the results demonstrate good tracking performance in construction scenarios with complex background. The average distance error and F-measure for the MDNet are 7.64 pixels and 67, respectively. The results demonstrate that the proposed approach can be used by site managers to monitor and track workers for hazard prevention in construction sites.},
keywords={},
doi={10.1587/transinf.2022DLP0045},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Computer Vision-Based Tracking of Workers in Construction Sites Based on MDNet
T2 - IEICE TRANSACTIONS on Information
SP - 653
EP - 661
AU - Wen LIU
AU - Yixiao SHAO
AU - Shihong ZHAI
AU - Zhao YANG
AU - Peishuai CHEN
PY - 2023
DO - 10.1587/transinf.2022DLP0045
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - Automatic continuous tracking of objects involved in a construction project is required for such tasks as productivity assessment, unsafe behavior recognition, and progress monitoring. Many computer-vision-based tracking approaches have been investigated and successfully tested on construction sites; however, their practical applications are hindered by the tracking accuracy limited by the dynamic, complex nature of construction sites (i.e. clutter with background, occlusion, varying scale and pose). To achieve better tracking performance, a novel deep-learning-based tracking approach called the Multi-Domain Convolutional Neural Networks (MD-CNN) is proposed and investigated. The proposed approach consists of two key stages: 1) multi-domain representation of learning; and 2) online visual tracking. To evaluate the effectiveness and feasibility of this approach, it is applied to a metro project in Wuhan China, and the results demonstrate good tracking performance in construction scenarios with complex background. The average distance error and F-measure for the MDNet are 7.64 pixels and 67, respectively. The results demonstrate that the proposed approach can be used by site managers to monitor and track workers for hazard prevention in construction sites.
ER -
English
