IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v52y2023i3p1239-1287.html
   My bibliography  Save this article

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

Author

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

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.

Suggested Citation

  • Wim Bernasco & Evelien M. Hoeben & Dennis Koelma & Lasse Suonperä Liebst & Josephine Thomas & Joska Appelman & Cees G. M. Snoek & Marie Rosenkrantz Lindegaard, 2023. "Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing," Sociological Methods & Research, , vol. 52(3), pages 1239-1287, August.
    • Handle: RePEc:sae:somere:v:52:y:2023:i:3:p:1239-1287
    DOI: 10.1177/00491241221099554
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/00491241221099554
    Download Restriction: no
    File URL: https://libkey.io/10.1177/00491241221099554?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:somere:v:52:y:2023:i:3:p:1239-1287. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.