2024 Volume 80 Issue 22 Article ID: 23-22026
In the after-coronas, there is still a high demand for information on the degree of human crowding because of the importance of continuing infection control measures for the elderly, who are at high risk of serious illness. The technology to detect people using monitoring cameras is costly in terms of the number of cameras installed in wide areas, and has limitations in terms of coverage and installation locations. In this study, we propose a method for displaying and updating the distribution of people using images obtained by recording live streaming video from an omnidirectional camera while moving. The human distribution was qualitatively visualized by obtaining and mapping the positions of people on a floor map of a facility using alignment and machine-learning-based human detection and tracking processes. As a result, the system was able to display and update the distribution of people online in parallel, even over a wide area, demonstrating the usefulness of the system in practical use.
