On the Complexity of Object Detection on Real-world Public Transportation Images for Social Distancing Measurement
This addresses the challenge of social distancing measurement in public transport settings, which is incremental as it applies existing methods to a new domain with unique complexities.
The paper tackles the problem of measuring social distancing on public transportation by benchmarking state-of-the-art object detection algorithms on real-world footage from the London Underground and bus network, showing improvements over vanilla methods by incorporating domain knowledge of passenger behavior.
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, to date, there has been no study of social distance measurement on public transport. The public transport setting has some unique challenges, including some low-resolution images and camera locations that can lead to the partial occlusion of passengers, which make it challenging to perform accurate detection. Thus, in this paper, we investigate the challenges of performing accurate social distance measurement 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 measurement 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.