Quantifying social distancing compliance and the effects of behavioral interventions using computer vision

Abstract

Social distancing has become a pressing and challenging issue during the Covid-19 pandemic. In a smart cities context, it becomes possible to measure inter-personal distance using networked cameras and computer vision analysis. We deploy a computer vision pipeline based on Retinanet that identifies pedestrians in streaming video frames, then converts their positions to GPS coordinates for distance calculation and further analysis. This processing is applied to nine camera streams at three locations from around Vanderbilt University. We collect 70 hours of baseline distancing data over the course of two weeks, after which time we deploy small behavioral interventions at the three locations aimed at increasing distancing compliance. Another 70 hours of data with the interventions in place will be analyzed against the baseline data to determine if they had an effect on distancing compliance.