The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
생산성 평가, 불안전한 행동 인식 및 진행 모니터링과 같은 작업에는 건설 프로젝트와 관련된 객체의 자동 연속 추적이 필요합니다. 많은 컴퓨터 비전 기반 추적 접근 방식이 건설 현장에서 조사되고 성공적으로 테스트되었습니다. 그러나 실제 적용은 건설 현장의 역동적이고 복잡한 특성(예: 배경, 폐색, 다양한 규모 및 포즈가 있는 혼란)으로 인해 추적 정확도가 제한되어 방해를 받습니다. 더 나은 추적 성능을 달성하기 위해 MD-CNN(Multi-Domain Convolutional Neural Networks)이라는 새로운 심층 학습 기반 추적 접근 방식이 제안되고 조사되었습니다. 제안된 접근 방식은 두 가지 주요 단계로 구성됩니다. 1) 학습의 다중 도메인 표현; 2) 온라인 시각적 추적. 이 접근 방식의 효과와 실행 가능성을 평가하기 위해 중국 우한의 지하철 프로젝트에 적용했으며 그 결과 복잡한 배경을 가진 건설 시나리오에서 우수한 추적 성능을 보여줍니다. MDNet의 평균 거리 오차와 F-측정값은 각각 7.64픽셀과 67픽셀입니다. 결과는 제안된 접근법이 현장 관리자가 건설 현장의 위험 예방을 위해 작업자를 모니터링하고 추적하는 데 사용할 수 있음을 보여줍니다.
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.
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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
부
@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},}
부
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 -