It's being built as we speak. I attended at a city council meeting yesterday, discussing approving a contract for ALPR cameras. I learned about a product from the camera vendor called Fusus[0], a dashboard that integrates various camera systems, ALPRs, alerts, etc. Two things stood out to me: natural-language querying of video feeds, and future planned integration with civilian-deployed cameras. The city only had budget for 50 ALPRs, and they stressed how they're only deploying them on main streets, but it seems like only a matter of time before your neighbor is able to install a camera that feeds right into the local PD's AI-enabled systems. One council member raised concerns about integrations with the citizen app[1] specifically (and a few others I didn't catch the names of). I'm very worried about where all this is heading.

[0]: https://www.axon.com/products/axon-fusus [1]: https://citizen.com/

I live in Oxford, UK and walked past a police van that said "automatic facial recognition in use". Not exactly a good sign without any caveats. I imagine they recorded me staring at their van.

Totally valid concern. Right now the cost ($2.50/hr) and latency make continuous real-time indexing impractical, but that won't always be the case. This is one of the reasons I'd want to see open-weight local models for this, keeps the indexing on your own hardware with no footage leaving your machine. But you're right that the broader trajectory here is worth thinking carefully about.

It's 2.50 an hour because Google has margins. A nation state could do it at cost, and even if it's not a huge difference, the price of a year's worth of embeddings is just $21,900. That's a rounding error, especially considering it's a one time cost for footage.

Right? $2.50 an hour is trivial to a Government that can vote to invent a trillion dollars. Even just 1 million dollars is the cost of monitoring 45 real time feeds for a year. I'm sure just many very rich people would pay that for the safety of their compound.

How are you getting to $2.50/hr ? The price sheet says its 0.00079 per frame.

https://ai.google.dev/gemini-api/docs/pricing#gemini-embeddi...

From what I see the code downsamples video to 5 fps, so 1 hour of video is 3600 seconds * 5 fps = 18,000 frames. 18,000 frames * $0.00079/frame = $14.22. A couple dollars more with the overlap.

(The code also tries to skip "still" frames, but if your video is dynamic you're looking at the cost above.)

you're right that the code uses ffmpeg to downsample the chunks to 5fps before sending them, but that's only a local/bandwidth optimization, not what the api actually processes.

regardless of the file's frame rate, the gemini api natively extracts and tokenizes exactly 1 fps. the 5 fps downscaling just keeps the payload sizes small so the api requests are fast and don't timeout.

i'll update the readme to make this more clear. thanks for bringing this up.

Thanks for the details and correction.

Most cameras are also not queryable by any one person or organization. They are owned by different companies and if the government wants access they have to subpoena them after the fact.

The problems start cropping up when you get things like Flock where governments start deploying cameras on a massive scale, or Ring where a single company has unrestricted access to everyone's private cameras.

I think Flock is just a symptom of the underlying tech becoming so cheap that "just blanket the city in cameras" starts to sound like a viable solution when police rely so heavily on camera footage.

I don't think it's a good thing but it seems the limiting factor has been technological feasibility instead of any kind of principle against it.

For specific people they probably wouldn’t use general embeddings. These embeddings can let you search for “tall man in a trenchcoat” but if you want a specific person you would use facial recognition.

I think a general description is better for surveillance/tracking like this, no? If they're at a weird angle or intentionally concealing their face then facial recognition falls apart but being able to describe them naturally would result in better tracking IMO.

Presumably the ideal is some kind of a fusion. Upload or tag some images/videos and link someone's social profiles and the system can look out for them based on facial recognition, gait recognition, vehicle/pets/common wardrobe items in combination.

Yeah, the panopticon is now technically very feasible it's just expensive to implement (for now).

Its very cheap to target an individual though so they dont need to look everywhere

All the major cloud providers offer some form of face detection and numberplate reading, with many supporting object detection (ie package, vehicle, person) out of the camera itself.

It's definitely creeping into things, though most of the features I've seen are fairly simplistic compared to what would be possible if the video was being reviewed + indexed by current SoTA multimodal LLMs.

Once the hardware to run inference for something like the vision understanding module of this can be run on a low / medium power asic drones are going to be absolutely horrifying weapons.

> this exact use case is sort of also my biggest point of concern with where AI takes us, much more so than most of the common AI risks you hear lots of chatter about.

I've been hearing warnings that AI would be used for this since well before it seemed feasible.

Not claiming to have hit on something unique here, but I think it’s realistic and often drowned out in favor of sci-fi nonsense.