This is a really cool implementation—embeddings still often feel like magic to me. That said, 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. We live in a world absolutely loaded with cameras now but ultimately retain some semblance of semi-anonymity/privacy in public by virtue of the fact that nobody can actually watch or review all of the video from those cameras except when there is a compelling reason to do so, but these technologies are making that a much more realistic proposition.
The presence of cameras everywhere is considerably more concerning than the status quo, to me at least, when there is an AI watching and indexing every second of every feed—where camera owners or manufacturers or governments could set simple natural language parameters for highly specific people or activities notify about. There are obviously compelling and easy-to-sell cases here that will surely drive adoption as it becomes cost effective: get an alert to crime in progress, get an alert when a neighbor who doesn't clean up after his dog, get an alert when someone has fallen...but the potential implications of living in a panopticon like this if not well regulated are pretty ugly.
Very impressive! A webhook could be configured to trigger an alarm if a semantic match to any category of activities is detected, and then you basically have a virtual security guard and private investigator. Well played.
Very interesting (not for a dashcam, but for home monitoring).
What a brilliant idea! is this all done locally? That's incredible.
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.)
Where is the Exit to this dystopia?
I don’t think this means we’re in a dystopia
In the matrix the exit was pay phones, which perhaps explains why our overlords are removing them
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.
I was thinking more of the season 3 episode of Doctor Who titled Gridlock where everyone lives in flying cars circling a giant expressway underground, while all the upper class people on the surface died years ago from a pandemic.
Ever get the feeling that the universe is reading your mind? Maybe there's some truth to that after all.
Does anyone know of an open weights models that can embed video? Would love to experiment locally with this.
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.
That's quite interesting, well done! I haven't thought of this use case for embeddings. It open the door to quite many potential applications!
Man, the surveillance applications for this are staggering.
> No transcription, no frame captioning, no intermediate text.
If there is text on the video (like a caption or wtv), will the embedding capture that? Never thought about this before.
If the video has audio, does the embedding capture that too?
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.
Today I learned that Gemini can now natively embed video..
Cool Project, thanks for sharing!
Nice use of native video embedding. How do you handle cases where Gemini's response confidence is low? Do you have a fallback or threshold?
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
very cool, anybody have apparent use cases for this?
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
Branding/marketing monitoring companies would be all over this.
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.
State surveillance
Worker surveillance
why not skip the text conversion? is it usable at all?
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
What are the SoA audio models right now?