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Indoor Trajectory Reconstruction Using Building Information Modeling and Graph Neural Networks

  • Mingkai Li
  • Peter Kok-Yiu Wong
  • Cong Huang
  • Jack C. P. Cheng

Trajectory reconstruction of pedestrian is of paramount importance to understand crowd dynamics and human movement pattern, which will provide insights to improve building design, facility management and route planning. Camera-based tracking methods have been widely explored with the rapid development of deep learning techniques. When moving to indoor environment, many challenges occur, including occlusions, complex environments and limited camera placement and coverage. Therefore, we propose a novel indoor trajectory reconstruction method using building information modeling (BIM) and graph neural network (GNN). A spatial graph representation is proposed for indoor environment to capture the spatial relationships of indoor areas and monitoring points. Closed circuit television (CCTV) system is integrated with BIM model through camera registration. Pedestrian simulation is conducted based on the BIM model to simulate the pedestrian movement in the considered indoor environment. The simulation results are embedded into the spatial graph for training of GNN. The indoor trajectory reconstruction is implemented as GNN conducts edge classification on the spatial graph

  • Keywords:
  • Indoor trajectory reconstruction; Graph neural network; Building information modeling; Camera-based tracking; Spatial graph; Pedestrian simulation,
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Mingkai Li

The Hong Kong University of Science and Technology, Hong Kong - ORCID: 0000-0002-3617-4083

Peter Kok-Yiu Wong

The Hong Kong University of Science and Technology, Hong Kong - ORCID: 0000-0003-1758-675X

Cong Huang

The Hong Kong University of Science and Technology, China - ORCID: 0009-0007-0915-8639

Jack C. P. Cheng

University of Hong KongThe Hong Kong University of Science and Technology, Hong Kong - ORCID: 0000-0002-1722-2617

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  • Publication Year: 2023
  • Pages: 895-906

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  • Publication Year: 2023

Chapter Information

Chapter Title

Indoor Trajectory Reconstruction Using Building Information Modeling and Graph Neural Networks

Authors

Mingkai Li, Peter Kok-Yiu Wong, Cong Huang, Jack C. P. Cheng

DOI

10.36253/979-12-215-0289-3.89

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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