INCREMENTAL MAPPING OF PEOPLE'S DISTRIBUTION WITH A MOBILE OMNIDIRECTIONAL CAMERA

Abstract

 Under the COVID-19 infection, consumer visits to tourist attractions and commercial establishments declined, which took a heavy toll on the economy. On the other hand, avoiding crowding prevents transmission of not only COVID-19 but also other infectious diseases, and the density of people in public and commercial facilities is likely to continue to affect the behavior of citizens. It is essential to reduce the risk of congestion without restricting people's behavior, and there is a growing demand for information on congestion levels. Existing technologies that visualize congestion by color-coding using motion sensors have the disadvantage that the visualized content could be more abstract, making it difficult to grasp the congestion situation. This study proposes a method to visualize the distribution of people while moving around the site using images captured by a 360° view camera. SfM can reconstruct the 3D shape of the target space and the shooting viewpoint, and a machine learning discriminator is used to extract and track people and map them into the space. This paper demonstrates the visualization of human crowding levels at various locations on a university campus.