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.

社会科学家越来越多地使用视频数据，但对其内容的大规模分析往往受到稀缺的人工编码资源的限制。随着计算机视觉领域正在开发的自动编码程序的应用，升级可能是可能的。在这里，我们向社会科学家介绍计算机视觉，回顾相关子领域的最新技术，并提供一个工作示例，说明计算机视觉如何应用于实证社会学工作。我们的应用程序包括由人类编码人员定义基本事实、开发自动编码算法、针对基本事实测试算法的性能，以及在大规模闭路电视图像数据集上运行算法。工作示例涉及在 COVID-19 大流行一年多的时间里监测公共场所的社交距离行为。最后，我们讨论了在实证社会科学研究中使用计算机视觉的前景，并解决了技术和伦理挑战。