Over the previous few many years, surveillance cameras, often known as closed-circuit tv (CCTV) cameras, have grow to be extensively utilized by governments, regulation enforcement officers and personal residents to watch public areas, forestall crime and establish criminals. Whereas the hundreds of thousands of surveillance cameras put in worldwide can play a vital function within the prevention of crime and assist police investigations, they will additionally significantly prohibit the privateness of residents.
When mixed with rising facial recognition expertise, surveillance cameras can grow to be much more intrusive, as they allow the identification, monitoring and monitoring of people. Furthermore, superior surveillance techniques may hinder freedom of speech, by dissuading individuals from collaborating in public gatherings or protests for worry of being recognized and persecuted.
Whereas some research have estimated the quantity of CCTV cameras put in in cities worldwide, only some have recognized their precise places. This makes it tougher to evaluate the influence of large-scale surveillance techniques and the extent to which they is perhaps invading the privateness of residents.
Researchers at Stanford College have lately carried out a examine aimed toward investigating the prevalence and places of surveillance cameras in massive cities within the U.S. and in different international locations worldwide. Their paper, introduced on the AAAI/ACM Convention on Synthetic Intelligence, Ethics and Society, introduces a pc imaginative and prescient algorithm that may estimate the spatial distribution of surveillance cameras by analyzing Google road view and different road view photographs.
“Our predominant aim was to grasp the quantity and site of surveillance cameras in massive cities all over the world,” Hao Sheng, one of many researchers who carried out the examine, informed TechXplore. “As a result of accumulating such knowledge manually is commonly prohibitively costly, we sought to develop strategies that could possibly be simply scaled up. As digital documentation of city landscapes and laptop imaginative and prescient expertise have each superior significantly in recent times, we thought that making use of laptop imaginative and prescient algorithms to current street-view photographs is perhaps potential.”
Of their examine, Sheng and his colleagues adopted three key steps. Firstly, they extracted road view photographs of 100,000 randomly sampled places in every of the cities they examined. They particularly centered on 10 massive cities within the U.S. (LA, New York, Chicago, Philadelphia, Seattle, Milwaukee, Baltimore, Washington D.C., San Francisco and Boston) and 6 different cities worldwide (Tokyo, Bangkok, London, Seoul, Singapore and Paris).
Subsequently, the researchers ran a pc imaginative and prescient algorithm on the road view photographs they extracted to mechanically detect surveillance cameras captured in them. Lastly, they requested human individuals to flick through the pictures and confirm the validity of the outcomes gathered by the algorithm (i.e., affirm whether or not it precisely noticed the cameras).
“Our methodology combines the deserves of laptop imaginative and prescient fashions (which could be rapidly deployed on hundreds of thousands of photographs) and people (who can visually establish cameras with larger accuracy),” Sheng defined. “So, even when cameras solely comprise a small share of road view photographs, we will nonetheless effectively and precisely establish them.”
The analyses carried out by Sheng and his colleagues yielded a number of attention-grabbing outcomes. Firstly, the researchers discovered that the density of cameras in cities was extremely correlated with the particular makes use of of given places and with the racial profile of neighborhoods. As an illustration, they discovered that cameras have been extra prone to be put in in a metropolis’s business, industrial and blended areas than in public or residential areas.
“Even after controlling for land use, we discovered a a lot larger density of cameras in majority-minority neighborhoods than in predominantly white neighborhoods,” Sheng mentioned. “We’re nonetheless attempting to grasp the mechanism that drives these patterns, however our findings counsel that communities of colour are disproportionately surveilled.”
The findings gathered by this staff of researchers may have essential implications for the long run set up of CCTV cameras in city environments. As an illustration, they might spark moral debates concerning the causes for the extraordinary monitoring of racial minorities or common discussions concerning the influence of large-scale surveillance on citizen’s privateness.
Of their subsequent research, Sheng and his colleagues plan to make use of the pc imaginative and prescient algorithm they developed to look at the prevalence of different varieties of cameras as properly, akin to doorbell cameras. Doorbell cameras, akin to Google Nest and Amazon Ring, permit individuals to see whether or not somebody is on the door and remotely talk with guests by way of their smartphone. In recent times, these good doorbell techniques have grow to be significantly widespread, significantly in residential neighborhoods.
“Some research estimate that the variety of doorbell cameras might have surpassed conventional surveillance cameras,” Sheng mentioned. “Measuring their prevalence will thus additional our understanding of the extent of surveillance in our communities. We additionally suspect that they might be good proxies of social belief in a neighborhood. In fact, doorbell cameras are often smaller, thus tougher to establish from road view photographs, which could pose new challenges for our digital camera detection course of.”
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Ethics and SocietySurveilling surveillance: estimating the prevalence of surveillance cameras with road view knowledge. Proceedings of the AAAI/ACM Convention on Synthetic Intelligence. arXiv:2105.01764 [cs.CY]. arxiv.org/abs/2105.01764
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Examine estimates the prevalence of CCTV cameras in massive cities worldwide (2021, June 16)
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