Egocentric monocular construction worker pose estimation for intelligent construction
department or school: Applied Science, Faculty of
department or school: Civil Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Suen, Wun Ki (Christine)
issued: 2024-04-15T18:35:57Z
issued: 2024
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: The hazardous working environment of construction workers leads to fatal accidents, whereas it is challenging to balance worker safety and productivity. Egocentric (i.e., first-person view) pose estimation is effective in identifying, localizing, and tracking worker poses under construction scenarios. It can be used as a human-centric approach to reduce fatalities and increase productivity through various downstream applications within Intelligent Construction, such as safety monitoring and human-robot collaboration. However, most of the egocentric pose estimation models are trained with daily life poses and have limited generalization ability to construction poses. The scarcity of publicly available datasets designated for egocentric worker pose estimation further impedes the training and evaluation of existing and newly developed egocentric worker pose estimation models. In this regard, this thesis presents a dual-modules egocentric worker pose estimation approach, which effectively addresses the major issues of self-occlusion and depth ambiguity in worker pose estimation. The proposed approach employs a supervised 2D module to address self-occlusion in construction poses and an unsupervised 3D module to handle depth ambiguity using only monocular-view input. It not only achieves a PCKh@0.5 of 98.7% in 2D pose estimation and a N-MPJPE of 96.43mm in 3D pose estimation, but also demonstrates strong generalization ability in the construction domain. To address the significant gap in egocentric pose estimation dataset for construction environments, this thesis also introduces ICON-Pose, the first open dataset includes thousands of egocentric images and corresponding 2D and 3D worker poses data across 63 action types. ICON-Pose accurately depicts complex construction worker poses with the proposed approach and shows a high level of diversity in action types, data forms, participants, and construction site settings. This dataset has the potential to serve as a general b…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/87827
note: Egocentric Monocular ConstructionWorker Pose Estimation for IntelligentConstructionbyWun Ki (Christine) SuenBSc., The Hong Kong Polytechnic University, 2021A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Civil Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)April 2024© Wun Ki (Christine) Suen 2024The following individuals certify that they have read, and recommend to theFaculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Egocentric Monocular Construction Worker Pose Estimation for Intel-ligent Constructionsubmitted by Wun Ki (Christine) Suen in partial fulfillment of the require-ments for the degree of Master of Applied Science in Civil Engineering.Examining Committee:Zhengbo Zou, Assistant Professor, Department of Civil Engineering, UBCSupervisorHelge Rhodin, Professor, Faculty of Technology, Bielefeld University;Adjunct Professor, Department of Computer Science, UBCSupervisory Committee MemberiiAbstractThe hazardous working environment of construction workers leads to fatal acci-dents, whereas it is challenging to balance worker safety and productivity. Ego-centric (i.e., first-person view) pose estimation is effective in identifying, localiz-ing, and tracking worker poses under construction scenarios. It can be used asa human-centric approach to reduce fatalities and increase productivity throughvarious downstream applications within Intelligent Construction, such as safetymonitoring and human-robot collaboration. However, most of the egocentric poseestimation models are trained with daily life poses and have limited generaliza-tion ability to construction poses. The scarcity of publicly available datasetsdesignated for egocentric worker pose estimation further impedes the trainingand evaluation of existing and newly developed egocentric worker pose estimationmodels.In this regard, this thesis presents a dual-modules egoc…
has type: Thesis/Dissertation
date issued: 2024-05
is shown at: 10.14288/1.0441337
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Zou, Zhengbo
type: Text
identifier URI: http://hdl.handle.net/2429/87827