Researchers at Stanford University recently conducted a study aimed at investigating the prevalence and locations of surveillance cameras in large cities in the U.S. and in other countries, Tech Xplore reports. Their paper introduces a computer vision algorithm that can estimate the spatial distribution of surveillance cameras by analysing Google street view and other street-view images. First, the researchers extracted street-view images of 100,000 randomly sampled locations in each of the cities they examined. They focused on 10 large cities in the U.S. (LA, New York, Chicago, Philadelphia, Seattle, Milwaukee, Baltimore, Washington D.C., San Francisco, and Boston) as well as Tokyo, Bangkok, London, Seoul, Singapore, and Paris. Subsequently, the researchers ran a computer-vision algorithm on the street-view images they extracted to automatically detect surveillance cameras. Finally, they had human participants browse through the images and verify the validity of the results gathered by the algorithm. The researchers found that the density of cameras in cities was highly correlated with the specific uses of given locations and with the racial profile of neighbourhoods. For instance, they found that cameras were more likely to be installed in a city’s commercial, industrial, and mixed areas than in public or residential areas.