Pentagon testing mass surveillance balloons across the US (2019)
theguardian.com
theguardian.com
I don't know if this was common knowledge or not: but the effect is you can continuously stare at incredibly large swaths of land at once, with the illusion of a pseudo-100+ Gbps video feed, through the right combination of vehicle/optics/camera/signal processing.
I seriously doubt that even with black-projects AI assistance. I have no experience with hi-altitude fancy photography, but a lot with the limits of terrestrial photography of human subjects.
https://www.axis.com/learning/web-articles/identification-an... Suggests something like a 2mm-4mm GSD would be required.
The small city I live in has an area of 179km^2. Assuming it’s a square and we’re taking a square photo, that’s 13.37km x 13.37km (nice number!). In each axis this is 13.37e3 m / 2e-3 m/px = 66.85e6 pixels. Squaring that gives us… if I’m counting right, a 4.4 Exapixel image. If we used the 4mm GSD instead of the 2mm GSD it would still be a 1.1 Exapixel image.
On a modest budget (maybe $200-300k) the kit I work with could be scaled up to do real-time gigapixel imaging at maybe 20-30 fps. But that’s still short by a factor of 1M both in image sensor real estate and in processing capacity.
I agree that this seems implausible. That being said, the gigapixel rig could still be quite scary in the wrong hands, just not at the city scale. You could do video that covers about a 350ft x 350ft area and be able to retroactively go back and do face identification on everyone whose faces you can see. It’s a completely parallel problem too and the costs scale linearly with the area unless you start running into second-order effects like diffraction limits or obliquely angles.
Edit: option 2 now that I think about it a little more would be to have a lower resolution broad image with servoable high resolution units. Point the 350x350ft camera at a specific ROI and filter down from there. That $200-300k budget includes about a dozen onboard high performance ML engines. Depending on the actual frame rate you want processed, you could either do light object recognition at a reasonable rate, or heavy inference at maybe 0.5-1fps.
It's probably lame to double down on this point, but, gigapixels is definitely enough to contiguously track people back to their homes and workplaces, which gives you like 90% of the benefit of facial recognition (for, as you point out, 1e-6 of the cost).
And yeah, continuous tracking of people is definitely viable, including entire crowds of people. A couple of years ago our local football team did a gigapixel photo of the entire stadium (on a 360 degree pan mount) and you could very easily manually pan through the photo and identify faces very very clearly. Tech to do that in real-time is definitely here and readily available.
(then again, if it is, presumably the US wouldnt simply hand them back "as is", so not sure what kind of endgame that would serve)
It's written right there. Made in China. The only thing still made in US is military equipment.