Chapter Indoor Trajectory Reconstruction Using Building Information Modeling and Graph Neural Networks
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 developme...
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| Main Authors: | , , , |
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| Format: | Online |
| Language: | English |
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Firenze University Press
2024
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| Subjects: | |
| Online Access: | ONIX_20240402_9791221502893_12 |
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| _version_ | 1869516724601094144 |
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| author | Li, Mingkai Wong, Peter Kok-Yiu Huang, Cong Cheng, Jack C. P. |
| author_browse | Cheng, Jack C. P. Huang, Cong Li, Mingkai Wong, Peter Kok-Yiu |
| author_facet | Li, Mingkai Wong, Peter Kok-Yiu Huang, Cong Cheng, Jack C. P. |
| author_sort | Li, Mingkai |
| collection | Directory of Open Access Books |
| description | 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 |
| format | Online |
| id | doab-20.500.12854ir-137013 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1370132024-05-11T08:47:07Z Chapter Indoor Trajectory Reconstruction Using Building Information Modeling and Graph Neural Networks Li, Mingkai Wong, Peter Kok-Yiu Huang, Cong Cheng, Jack C. P. Indoor trajectory reconstruction Graph neural network Building information modeling Camera-based tracking Spatial graph Pedestrian simulation thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization 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 2024-05-11T08:47:05Z 2024-05-11T08:47:05Z 2024-04-02T15:44:38Z 2023 chapter ONIX_20240402_9791221502893_12 2704-5846 https://library.oapen.org/handle/20.500.12657/89043 9791221502893 https://directory.doabooks.org/handle/20.500.12854/137013 eng Proceedings e report open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/89043/1/9791221502893_89.pdf Firenze University Press 10.36253/979-12-215-0289-3.89 10.36253/979-12-215-0289-3.89 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221502893 12 Florence open access |
| spellingShingle | Indoor trajectory reconstruction Graph neural network Building information modeling Camera-based tracking Spatial graph Pedestrian simulation thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization Li, Mingkai Wong, Peter Kok-Yiu Huang, Cong Cheng, Jack C. P. Chapter Indoor Trajectory Reconstruction Using Building Information Modeling and Graph Neural Networks |
| title | Chapter Indoor Trajectory Reconstruction Using Building Information Modeling and Graph Neural Networks |
| title_full | Chapter Indoor Trajectory Reconstruction Using Building Information Modeling and Graph Neural Networks |
| title_fullStr | Chapter Indoor Trajectory Reconstruction Using Building Information Modeling and Graph Neural Networks |
| title_full_unstemmed | Chapter Indoor Trajectory Reconstruction Using Building Information Modeling and Graph Neural Networks |
| title_short | Chapter Indoor Trajectory Reconstruction Using Building Information Modeling and Graph Neural Networks |
| title_sort | chapter indoor trajectory reconstruction using building information modeling and graph neural networks |
| topic | Indoor trajectory reconstruction Graph neural network Building information modeling Camera-based tracking Spatial graph Pedestrian simulation thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization |
| topic_facet | Indoor trajectory reconstruction Graph neural network Building information modeling Camera-based tracking Spatial graph Pedestrian simulation thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization |
| url | ONIX_20240402_9791221502893_12 |
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