Vision-based Health Protocol Observance System for Small Rooms
Silvan Vella, Daren Scerri · 2021
COVID-19 has impacted the daily lives of millions. Businesses and educational institutions had to take preventive measures including social distancing to reduce the spread of COVID-19. This study aims to mitigate COVID-19 transmission in a small areas like classrooms, where occlusion and perspective issues are prevalent and highly challenging, using an innovative vision-based approach. Several human-head detection YOLOv4 models were trained on three different training datasets. Afterwards, they got evaluated to select the most reliable model for the social distancing solution. A 91.12% mAP was reached after improving the SCUT-HEAD dataset by generating face masks on the subjects. Using euclidean distance, triangle similarity and head size ratios a formula was developed to accurately calculate distances in small spaces using one camera; with no need of perspective annotation and highly reducing occlusion issues. Given most studies reviewed lacked ground-truth data, we created a real test scenario, with marks on the floor to readily provide ground-truth data during the experiment. The proposed human-head (triangle similarity) method managed to achieve an F1-Score of 85.66% compared to a reference state-of-the-art solution employing a whole body and perspective transform approach which achieved an F1-Score of 80.46%. Our solution achieved better results in small room scenarios, with high prospects of addressing challenges in a real-world environment.