- CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
- A cura di Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Localizing and Visualizing the Degree of People Crowding with an Omnidirectional Camera by Different Times
- Tomu Muraoka
- Satoshi Kubota
- Yoshihiro Yasumuro
- © 2023 Author(s) |
- CC BY-NC 4.0
- DOI: 10.36253/979-12-215-0289-3.65
The Corona Disaster increased the demand for information on the degree of human crowding, as it was essential to balance avoiding restricting behavior and reducing the risk of crowding. Although there are many technologies for detecting people using monitoring cameras, the number of cameras installed in a wide area is costly, and coverage is limited. In this study, we propose a method to qualitatively visualize the distribution of people by using images captured by a moving omnidirectional camera from the viewpoint of facility management during regular security patrols. Omnidirectional images are used for both 3D modeling of the target space based on SfM (structure from motion) and person detection/tracking by machine learning. The distribution of people is visualized qualitatively by obtaining the positions of the extracted people on the 3D model of the site and mapping them. The parallel software processing of visitor observation and mapping is expected to be highly cost-effective in terms of implementation and operation. On the other hand, although there are time deviations in the mapping depending on the location, the visualization and the updated time show their usefulness in understanding the distribution of congestion
- Keywords:
- COVID-19,
- people's congestion,
- omnidirectional camera,
- SfM (Structure from Motion),
- machine-learning,
Kansai University, Japan
Kansai University, Japan
Kansai University, Japan - ORCID: 0000-0001-8317-3123
- National Institute of Infectious Diseases (2023), Ministry of Health, Labor and Welfare, iDWR Infectious Diseases Weekly Report，<https://www.niid.go.jp/niid/images/idsc/idwr/IDWR2023/idwr2023-20.pdf>，(Viewed 2023. 6.5).
- Yahoo! JAPAN (2023), Yahoo! JAPAN Map, Congestion Radar, <https://map.yahoo.co.jp/congestion>, (Viewed 2023.8.1).
- EXPOCITY (2023)，Current parking lot congestion, < https://www.expocity-mf.com/expo/parking/>, (Viewed 2023.9.21)
- Hitachi, Ltd (2020), Hitachi technology demonstration at Tokyo Dome for infection-prevention measures: Visualization of congestion inside the stadium, <https://social-innovation.hitachi/en/topics/tokyo-dome/> (Viewed 2023.8.3).
- T. Muraoka., S. Kubota, and Y. Yasumuro, (2022) Localizing and Mapping of People’s Distribution with an Omnidirectional Camera, Proceedings of the 22nd International Conference on Construction Application of Virtual Reality (CONVR2022), pp. 134-142.
- S. A. Eroglu, K. Machleit, and T. F. Barr (2005), Perceived Retail Crowding and Shopping Satisfaction: The Role of Shopping Values, Journal of Business Research, Vol 58 (8), pp. 1146-1153.
- M. A. Fischler, R. C. Bolles (1981). Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, Vol 24, pp 381-395.
- Z. Ge, S. Liu, F. Wang, Z. Li, J. Sun (2021), YOLO X: Exceeding YOLO Series in 2021, The Conference on Computer Vision and Pattern Recognition (CVPR2021).
- motpy - simple multi object tracking library, <https://github.com/wmuron/motpy> (Viewed 2022.10.26)
- K. S. Arum, T. Shuang and S. D. Blostein (1987), Least-Squares Fitting of Two 3-D Point Sets, IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol.9, No.5, pp. 698-700.
Informazioni sul capitolo
Titolo del capitolo
Localizing and Visualizing the Degree of People Crowding with an Omnidirectional Camera by Different Times
Autori
Tomu Muraoka, Satoshi Kubota, Yoshihiro Yasumuro
DOI
10.36253/979-12-215-0289-3.65
Opera sottoposta a peer review
Anno di pubblicazione
2023
Copyright
© 2023 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
Sottotitolo del libro
Managing the Digital Transformation of Construction Industry
Curatori
Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Opera sottoposta a peer review
Anno di pubblicazione
2023
Copyright
© 2023 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
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
Collana
Proceedings e report
ISSN della collana
2704-601X
e-ISSN della collana
2704-5846