Unbiased Population Size Estimation on Still Gigapixel Images

Marcos Cruz and Javier González-Villa

Sociological Methods & Research, 2021, vol. 50, issue 2, 627-648

Abstract: Population sizing is essential in sociology and in various other real-life applications. Gigapixel cameras can provide high-resolution images of an entire population in many cases. However, exhaustive manual counting is tedious, slow, and difficult to verify, whereas current computer vision methods are biased and known to fail for large populations. A design unbiased method based on geometric sampling has recently been proposed. It typically requires only between 50 and 100 manual counts to achieve relative standard errors of 5–10 percent irrespective of population size. However, the large perspective effect introduced by gigapixel images may boost the relative standard error to 30–40 percent. Here, we show that projecting the sampling grid from a map onto the gigapixel image using the camera projection neutralizes the variance due to perspective effects and restores the relative standard errors back into the 5–10 percent range. The method is tested on six simulated images. A detailed step-by-step illustration is provided with a real image of a 30,000 people crowd.

Keywords: crowd size; geometric sampling; demonstration; design unbiased method; population size; political rally; gigapixel image (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0049124118799373 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:50:y:2021:i:2:p:627-648

DOI: 10.1177/0049124118799373

Access Statistics for this article

More articles in Sociological Methods & Research
Bibliographic data for series maintained by SAGE Publications ().