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

TL;DR

This paper examines the challenges of measuring social distancing in public transportation images, benchmarking detection algorithms on real-world footage, and proposing strategies to improve detection accuracy considering unique transport environment challenges.

Contribution

It is the first study to analyze social distance measurement on public transport images, benchmarking algorithms and proposing domain knowledge-based improvements.

Findings

Detection accuracy is challenged by low-resolution and occlusions.

Benchmarking reveals varying performance of state-of-the-art detectors.

Domain knowledge strategies improve detection results.

Abstract

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.