Show HN: Gemini can now natively embed video, so I built sub-second video search

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/

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.

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.

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

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.

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.

> 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.

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.

Nice article. I saw someone depicting the future of web search with AI. The conclusion was not the bright future. Simply put, ads will never go away. Either AI providers will get paid for whitelisting ads, or even worse these AI will directly promote advertised products.

Yeah, this is a great idea, I’ve actually been thinking about exactly this as the next logical step.

SentrySearch already returns precise in/out timestamps for any natural-language query and uses ffmpeg to auto-trim clips. Turning that into an EDL (or even a direct Premiere plugin that exports an editable cut list) feels natural.

I’m not a Premiere expert myself, but I’d love to see this happen. If you (or anyone) wants to sketch out a quick EDL exporter or plugin, I’ll happily review + merge a PR and help wherever I can. Just drop a GitHub issue if you start something!

Most home monitoring only records when there is movement though? So that already compresses the search space a lot. And just zipping forward and back it's pretty easy to quickly find the 30 seconds where there is a figure wallking up to your front door.

this function will be a must-have for all home security systems. I used to spend hours going through home security cameras to check if our cat went out the house when the door was accidentally left open (turned out it was just really good at hiding within the house).

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!

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.

ur not wrong

yea, it's so events on a chunk boundary still get captured in at least one chunk. i haven't had the chance to do formal benchmarks on overlap vs. no-overlap yet. the 5s default is a pragmatic choice, long enough to catch most events that would otherwise be split, short enough to not add much cost (120 chunks/hr to ~138). also it's configurable via the --overlap flag.

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.)

Make a proof of concept, honestly worked fairly well: https://github.com/jakejimenez/sentinelsearch

The README on the GitHub has a section on this[0]:

>Indexing 1 hour of footage costs ~$2.84 with Gemini's embedding API (default settings: 30s chunks, 5s overlap):

>1 hour = 3,600 seconds of video = 3,600 frames processed by the model. 3,600 frames × $0.00079 = ~$2.84/hr

>The Gemini API natively extracts and tokenizes exactly 1 frame per second from uploaded video, regardless of the file's actual frame rate. The preprocessing step (which downscales chunks to 480p at 5fps via ffmpeg) is a local/bandwidth optimization — it keeps payload sizes small so API requests are fast and don't timeout — but does not change the number of frames the API processes.

[0] https://github.com/ssrajadh/sentrysearch#cost

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

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.

A quick search brought up https://qwen.ai/blog?id=qwen3-vl-embedding but I have no idea if it does what Gemini is doing here.

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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.

haha no i'm driving the tesla and that clip is from the left repeater camera (teslas record from all around the car)

Indexing all your porn and skipping all the filler.

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.

Trail and game cams come to mind. "Create a montage of all deer encounters," "Find first appearance of black bear this year," that sort of thing.

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

dashcam is just one of the use cases and the one i tested on. but this could theoretically work with any kind of video footage like home security footage

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

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

You don’t wanna live in Night City?

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.