ABSTRACT
Thousands of infections, hundreds of deaths every day - these are numbers that speak the current serious status, numbers that each of us is no longer unfamiliar with in the current context, the context of the raging epidemic - Coronavirus disease epidemic. Therefore, we need solutions and technologies to fight the epidemic promptly and quickly to prevent or reduce the effect of the epidemic. Numerous studies have warned that if we contact an infected person within a distance of fewer than two meters, it can be considered a high risk of infecting Coronavirus. To detect a contact distance shorter than two meters and provides warnings to violations in monitoring systems based on a camera, we present an approach to solving two problems, including detecting objects - here are humans and calculating the distance between objects using Chessboard and bird's eye perspective. We have leveraged the pre-trained InceptionV2 model, a famous convolutional neural network for object detection, to detect people in the video. Also, we propose to use a perspective transformation algorithm for the distance calculation converting pixels from the camera perspective to a bird's eye view. Then, we choose the minimum distance from the distance in the determined field to the distance in pixels and calculate the distance violation based on the bird's eye view, with camera calibration and minimum distance selection process based on field distance. The proposed method is tested in some scenarios to provide warnings of social distancing violations. The work is expected to generate a safe area providing warnings to protect employees in administrative environments with a high risk of contacting numerous people. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Measures to contain COVID-19 pandemic in Taiwan included international travel restrictions since February 2020, which resulted in a nearly 80% reduction of aviation volume at the International Taoyuan Airport (TPE), while industry and ground traffic continued to operate unaffected by the pandemic. This study attempted to assess the contribution of aviation volume to air pollution measured by a monitoring station, located 2 km southwest to the airport. We applied cluster analysis to identify TPE contribution to the major air pollutants and estimated their relationship with the number of passengers as a proxy to the flights number. From the airport containing cluster, we observed significant reduction of air pollution concentrations after the travel restrictions. The reduction percentage of SO2 and NOx was higher in the airport cluster (17.7% and 7.3%, respectively) compared to the total station observation (14.7% and 6.8% respectively). Spearman's coefficients indicated positive significant correlations between the number of passengers and PM2.5 (0.06), PM10 (0.21), SO2 (0.24), especially after the travel restrictions. Such low correlations were found due to the distance of 2 km between the monitoring station and the airport runway. This distance could be too far to precisely detect the contribution of aviation to air pollution, which was masked by industrial activities and ground traffic. Measuring air pollution at a closer distance to the runways is required for a better catchment of aviation impact on air quality.