On the Complexity of Object Detection on Real-world Public Transportation Images for Social Distancing Measurement

Social distancing in public spaces has become an essential aspect in helping to reduce the impact of the COVID-19 pandemic. Exploiting recent advances in machine learning, there have been many studies in the literature implementing social distancing via object detection through the use of surveillance cameras in public spaces. However, there has been no study of social distance measurement on public transport to date. The public transport setting has some unique challenges, including low-resolution images and physical camera locations that can lead to the partial occlusion of passengers, making it challenging to perform accurate detection. Thus, this paper investigates the challenges of performing accurate social distance measurements on public transportation. We benchmark several state-of-the-art object detection algorithms using real-world footage taken from the London Underground and bus network. The work highlights the complexity of performing social distancing measurements on images from current public transportation onboard cameras. Further, exploiting domain knowledge of expected passenger behaviour, we attempt to improve the quality of the detections using various strategies and show improvement over using vanilla object detection alone.