A Real-Time Pedestrian Social Distancing Risk Alert System for COVID-19.

The COVID-19 virus is usually spread by small droplets when talking, coughing and sneezing, so maintaining physical distance between people is necessary to slow the spread of the virus. The World Health Organization (WHO) recommends maintaining a social distance of at least six feet. In this paper, we developed a real-time pedestrian social distance risk alert system for COVID-19, which monitors the distance between people in real-time via video streaming and provides risk alerts to the person in charge, thus avoiding the problem of too close social distance between pedestrians in public places. We design a lightweight convolutional neural network architecture to detect the distance between people more accurately. In addition, due to the limitation of camera placement, the previous algorithm based on flat view is not applicable to the social distance calculation for cameras, so we designed and developed a perspective conversion module to reduce the image in the video to a bird's eye view, which can avoid the error caused by the elevation view and thus provide accurate risk indication to the user. We selected images containing only person labels in the COCO2017 dataset to train our network model. The experimental results show that our network model achieves 82.3% detection accuracy and performs significantly better than other mainstream network architectures in the three metrics of Recall, Precision and mAP, proving the effectiveness of our system and the efficiency of our technology.