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oapen-20.500.12657-890672024-04-03T02:23:07Z Chapter Localizing and Visualizing the Degree of People Crowding with an Omnidirectional Camera by Different Times Muraoka, Tomu Kubota, Satoshi Yasumuro, Yoshihiro COVID-19 people's congestion omnidirectional camera SfM (Structure from Motion) machine-learning thema EDItEUR::U Computing and Information Technology 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 2024-04-02T15:45:29Z 2024-04-02T15:45:29Z 2023 chapter ONIX_20240402_9791221502893_36 2704-5846 9791221502893 https://library.oapen.org/handle/20.500.12657/89067 eng Proceedings e report application/pdf n/a 9791221502893_65.pdf https://books.fupress.com/doi/capitoli/979-12-215-0289-3_65 Firenze University Press 10.36253/979-12-215-0289-3.65 10.36253/979-12-215-0289-3.65 bf65d21a-78e5-4ba2-983a-dbfa90962870 9791221502893 137 12 Florence open access
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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
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