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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Promise Into Practice : Application of Computer Vision in Empirical Research on Social Distancing. / Bernasco, Wim; Hoeben, Evelien ; Koelman, Dennis; Liebst, Lasse Suonperä; Thomas, Josephine; Appelman, Joska; Snoek, Cees; Lindegaard, Marie Rosenkrantz.

In: Sociological Methods & Research, Vol. 52, No. 3, 2023, p. 1239-1287.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Bernasco, W, Hoeben, E, Koelman, D, Liebst, LS, Thomas, J, Appelman, J, Snoek, C & Lindegaard, MR 2023, 'Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing', Sociological Methods & Research, vol. 52, no. 3, pp. 1239-1287. https://doi.org/10.1177/00491241221099554

APA

Bernasco, W., Hoeben, E., Koelman, D., Liebst, L. S., Thomas, J., Appelman, J., Snoek, C., & Lindegaard, M. R. (2023). Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing. Sociological Methods & Research, 52(3), 1239-1287. https://doi.org/10.1177/00491241221099554

Vancouver

Bernasco W, Hoeben E, Koelman D, Liebst LS, Thomas J, Appelman J et al. Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing. Sociological Methods & Research. 2023;52(3):1239-1287. https://doi.org/10.1177/00491241221099554

Author

Bernasco, Wim ; Hoeben, Evelien ; Koelman, Dennis ; Liebst, Lasse Suonperä ; Thomas, Josephine ; Appelman, Joska ; Snoek, Cees ; Lindegaard, Marie Rosenkrantz. / Promise Into Practice : Application of Computer Vision in Empirical Research on Social Distancing. In: Sociological Methods & Research. 2023 ; Vol. 52, No. 3. pp. 1239-1287.

Bibtex

@article{935bacb2575d469f976298dc589e6344,
title = "Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing",
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 = "Faculty of Social Sciences, Computer vision, video data analysis, deep learning, pedestrian detection, social distancing",
author = "Wim Bernasco and Evelien Hoeben and Dennis Koelman and Liebst, {Lasse Suonper{\"a}} and Josephine Thomas and Joska Appelman and Cees Snoek and Lindegaard, {Marie Rosenkrantz}",
year = "2023",
doi = "10.1177/00491241221099554",
language = "English",
volume = "52",
pages = "1239--1287",
journal = "Sociological Methods and Research",
issn = "0049-1241",
publisher = "SAGE Publications",
number = "3",

}

RIS

TY - JOUR

T1 - Promise Into Practice

T2 - Application of Computer Vision in Empirical Research on Social Distancing

AU - Bernasco, Wim

AU - Hoeben, Evelien

AU - Koelman, Dennis

AU - Liebst, Lasse Suonperä

AU - Thomas, Josephine

AU - Appelman, Joska

AU - Snoek, Cees

AU - Lindegaard, Marie Rosenkrantz

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Faculty of Social Sciences

KW - Computer vision

KW - video data analysis

KW - deep learning

KW - pedestrian detection

KW - social distancing

U2 - 10.1177/00491241221099554

DO - 10.1177/00491241221099554

M3 - Journal article

VL - 52

SP - 1239

EP - 1287

JO - Sociological Methods and Research

JF - Sociological Methods and Research

SN - 0049-1241

IS - 3

ER -

ID: 300674712