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

Research output: Contribution to journalJournal articleResearchpeer-review


  • Full Text

    Final published version, 1.97 MB, PDF document

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.
Original languageEnglish
JournalSociological Methods & Research
Number of pages49
Publication statusPublished - 2023

    Research areas

  • Faculty of Social Sciences - Computer vision, video data analysis, deep learning, pedestrian detection, social distancing

ID: 300674712