- CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
- Edited by 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
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Chapter Information
Chapter Title
Localizing and Visualizing the Degree of People Crowding with an Omnidirectional Camera by Different Times
Authors
Tomu Muraoka, Satoshi Kubota, Yoshihiro Yasumuro
DOI
10.36253/979-12-215-0289-3.65
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
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
Metadata License
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