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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

  1. Asadi, K., Ramshankar, H., Noghabaei, M., & Han, K. (2019). Real-time image localization and registration with BIM using perspective alignment for indoor monitoring of construction. Journal of Computing in civil Engineering, 33(5), 04019031.
  2. Brasó, G., & Leal-Taixé, L. (2020). Learning a neural solver for multiple object tracking. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 6247-6257).
  3. Braun, A., Tuttas, S., Borrmann, A., & Stilla, U. (2020). Improving progress monitoring by fusing point clouds, semantic data and computer vision. Automation in Construction, 116, 103210.
  4. Cheng, D., Gong, Y., Zhou, S., Wang, J., & Zheng, N. (2016). Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In Proceedings of the iEEE conference on computer vision and pattern recognition (pp. 1335-1344).
  5. Cheng, J. C., & Gan, J. (2013). Integrating agent-based human behavior simulation with building information modeling for building design. International Journal of Engineering and Technology, 5(4), 473.
  6. Cheng, J. C., Kwok, H. H., Li, A. T., Tong, J. C., & Lau, A. K. (2022). BIM-supported sensor placement optimization based on genetic algorithm for multi-zone thermal comfort and IAQ monitoring. Building and Environment, 216, 108997.
  7. Cheng, J. C., Poon, K. H., & Wong, P. K. Y. (2022). Long-Time gap crowd prediction with a Two-Stage optimized spatiotemporal Hybrid-GCGRU. Advanced Engineering Informatics, 54, 101727.
  8. Deng, H., Hong, H., Luo, D., Deng, Y., & Su, C. (2020). Automatic indoor construction process monitoring for tiles based on BIM and computer vision. Journal of construction engineering and management, 146(1), 04019095.
  9. Kim, I., Galiza, R., & Ferreira, L. (2013). Modeling pedestrian queuing using micro-simulation. Transportation Research Part A: Policy and Practice, 49, 232-240.
  10. Lee, J. (2004). A spatial access-oriented implementation of a 3-D GIS topological data model for urban entities. GeoInformatica, 8, 237-264.
  11. Liu, S., Lo, S., Ma, J., & Wang, W. (2014). An agent-based microscopic pedestrian flow simulation model for pedestrian traffic problems. IEEE Transactions on Intelligent Transportation Systems, 15(3), 992-1001.
  12. Lukins, T. C., & Trucco, E. (2007, September). Towards Automated Visual Assessment of Progress in Construction Projects. In BMVC (pp. 1-10).
  13. Luo, H., Gu, Y., Liao, X., Lai, S., & Jiang, W. (2019). Bag of tricks and a strong baseline for deep person re-identification. In the IEEE/CVF conference on computer vision and pattern recognition workshops (pp. 0-0).
  14. Nauata, N., Chang, K. H., Cheng, C. Y., Mori, G., & Furukawa, Y. (2020, Fall). House-gan: Relational generative adversarial networks for graph-constrained house layout generation. In Computer Vision–ECCV 2020: 16th European Conference (Part I 16, pp. 162-177).
  15. Patron-Perez, A., Lovegrove, S., & Sibley, G. (2015). A spline-based trajectory representation for sensor fusion and rolling shutter cameras. International Journal of Computer Vision, 113(3), 208-219.
  16. Rebolj, D., Babič, N. Č., Magdič, A., Podbreznik, P., & Pšunder, M. (2008). Automated construction activity monitoring system. Advanced engineering informatics, 22(4), 493-503.
  17. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28.
  18. Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016). Performance measures and a data set for multi-target, multicamera tracking. European Conference on Computer Vision Workshops (EECVW), Amsterdam, The Netherlands (pp. 43–51).
  19. Said, H., Kandil, A., & Cai, H. (2012). Agent-based simulation of labour emergency evacuation in high-rise building construction sites. In Construction Research Congress 2012: Construction Challenges in a Flat World (pp. 1104-1113).
  20. Seyfried, A., Steffen, B., Klingsch, W., & Boltes, M. (2005). The fundamental diagram of pedestrian movement revisited. Journal of Statistical Mechanics: Theory and Experiment, 2005(10), P10002.
  21. Song, C., Chen, Z., Wang, K., Luo, H., & Cheng, J. C. (2022). BIM-supported scan and flight planning for fully autonomous LiDAR-carrying UAVs. Automation in Construction, 142, 104533.
  22. Traunmueller, M. W., Johnson, N., Malik, A., & Kontokosta, C. E. (2018). Digital footprints: Using WiFi probe and locational data to analyze human mobility trajectories in cities. Computers, Environment and Urban Systems, 72, 4-12.
  23. Troncoso-Pastoriza, F., López-Gómez, J., & Febrero-Garrido, L. (2018). Generalized vision-based detection, identification and pose estimation of lamps for BIM integration. Sensors, 18(7), 2364.
  24. Wong, P. K. Y., Luo, H., Wang, M., & Cheng, J. C. (2022). Enriched and discriminative convolutional neural network features for pedestrian re‐identification and trajectory modeling. Computer‐Aided Civil and Infrastructure Engineering, 37(5), 573-592.
  25. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Philip, S. Y. (2020). A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1), 4-24.
  26. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015). Scalable person re-identification: A benchmark. In Proceedings of the IEEE international conference on computer vision (pp. 1116-1124).
  27. Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI open, 1, 57-81.
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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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