I propose to create a typology of surveillance power by building facial recognition recognition. That is, I plan to build a computer vision pipeline that can automatically recognize and clippings of surveillance cameras from images of street scenes, an image classifier that categorizes these segmented images into the kind of camera, their technical capability for facial recognition, and the institution they are owned by and send data back to. This will enable me to automate the creation of both power maps and topographic maps of surveillance, that is: both representations of the relationships between institutions of power the surveillance acts on behalf of, as well as representations of the spatial locations different kinds of surveillance can be found.
Explanatory illustrations
I have an existing practice of walking around short stretches of Pittsburgh streets and exhaustively drawing every single surveillance camera I can see. This has allowed me to notice the particular different kinds of cameras: whether it be the telephone pole mounted ones that send data to Allegheny county, the Amazon Ring cameras that guard private homes and businesses, or the pan and tilt ones that create audible and motion presences outside of banks.
But this takes forever. What if computer vision did this for me from images taken of street scenes? Better yet, what if I used google street view images, so I could do this at a massive scale?
I want an inventory of every single camera visible from the street in Pittsburgh, along with who owns each, and what model of camera and what capabilities it has.