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/

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.

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.

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.

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

I've found I have to be very specific to get the clip I'm searching for. For example, "car cuts me off" just returned a clip of a car driving past my blindspot. But, "car with bike rack on back cuts me off at night" gave me exactly the clip I was looking for.

Well, with data analysis powers like this a few treasonous words in front of a flock camera will show you the way.

In the matrix the exit was pay phones, which perhaps explains why our overlords are removing them

I don’t think this means we’re in a dystopia

The dystopia of searching for video clips and finding them? What?

The Matrix style human pods: we live in blissful ignorance in the Matrix, while the LLMs extract more and more compute power from us so some CEO somewhere can claim they have now replaced all humans with machines in their business.

Thanks! Yeah that would be pretty cool, but continuous indexing would be pretty expensive now, because the model's in public preview and there are no local alternatives afaik.

This very well might be a reality in a couple years though!

While the vector store is local, it is sending the data to Gemini's API for embedding. (Which if using a paid API key is probably fine for most use cases, no long term retention/training etc.)

Not aware of any that do native video-to-vector embedding the way Gemini Embedding 2 does. There are CLIP-based models (like VideoCLIP) that embed frames individually, but they don't process temporal video. you'd need to average frame embeddings which loses a lot.

Would love to see open-weight models with this capability since it would eliminate the API cost and the privacy concern of uploading footage.

Man, the surveillance applications for this are staggering.

as of now, no threshold but that is planned in the future.

for example, for now if i search "cybertruck" in my indexed dashcam footage, i don't have any cybertrucks in my footage, so it'll return a clip of the next best match which is a big truck, but not a cybertruck

Yes to both. The embedding is over raw video frames, so anything visible (text, signs, captions) gets captured in the vector. And Gemini Embedding 2 extracts the audio track and embeds it alongside the visual frames. So a query like 'someone yelling' would theoretically match on audio. My dashcam footage doesn't have audio though, so I haven't tested that side yet.

dashcam and home security footage are the 2 main ones i can think of.

a bit expensive right now so it's not as practical at scale. but once the embedding model comes out of public preview, and we hopefully get a local equivalent, this will be a lot more practical.

I think a good use case would be searching for certain products or videos across social media (TikTok and Instagram). especially useful for shopping, maybe

State surveillance

gemini embedding 2 converts straight video to vectors. in this case, dashcam clips don't have audio to transcribe and even if they did, it would be useless in the search