Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

Wim Bernasco, Evelien M. Hoeben, Dennis Koelma, Lasse Suonperä Liebst, Josephine Thomas, Joska Appelman, Cees G. M. Snoek and Marie Rosenkrantz Lindegaard

Sociological Methods & Research, 2023, vol. 52, issue 3, 1239-1287

Abstract: Social scientists increasingly use video data, but large-scale analysis of its content is often constrained by scarce manual coding resources. Upscaling may be possible with the application of automated coding procedures, which are being developed in the field of computer vision. Here, we introduce computer vision to social scientists, review the state-of-the-art in relevant subfields, and provide a working example of how computer vision can be applied in empirical sociological work. Our application involves defining a ground truth by human coders, developing an algorithm for automated coding, testing the performance of the algorithm against the ground truth, and running the algorithm on a large-scale dataset of CCTV images. The working example concerns monitoring social distancing behavior in public space over more than a year of the COVID-19 pandemic. Finally, we discuss prospects for the use of computer vision in empirical social science research and address technical and ethical challenges.

Keywords: Computer vision; video data analysis; deep learning; pedestrian detection; social distancing (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:52:y:2023:i:3:p:1239-1287

DOI: 10.1177/00491241221099554

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