Sounds more like “vehicle recovery” for the repossession market first and foremost.
A repo investigator for the bank locates the target vehicle via owner’s social media, takes photo of the car, shoots it into GeoSpy, then ganks the car based on given locations in the owner’s photos. Pair it up with ALPR hits across a city from national ALPR networks (to help correlate home/business/work patterns) and… wellp, there you go!
Sure, an impressive bit of tech, but the potential for misuse is immense.
To mock their user reviews...
> “Graylark helped me find the person I'm stalking in under 20 minutes. This tool is unbelievable — a true game-changer for those with restraining orders like me who just want to get back at them for that court order."
our goal was to build technology to safeguard American freedom and prosperity...
...America deserves more. While Silicon Valley hype centers around LLMs, AGI, and SSI, our focus remains on visual intelligence—understanding the world we see with our eyes, what we call Visual Super Intelligence
Did I completely miss the technical aspect of this blog? They list an improvement but no details on how they achieved it. It sounds like a trained embedding model and a vector search. All told though this just reads as boring product talk.
Unless someone posts a photo of the stolen car with the numberplate still on, how would you identify YOUR car that way? Its not like cars are unique pieces. Same for bikes or anything else...
First things first, its entirely possible to geolocate using just visual markers.
A bunch of startups did it around 2018 (most got bought by facebook, ie mapillary) They work by extracting keypoints from pictures and building a massive point cloud of identifiable key points.
But
That picture they use with supposed keypoint matching is wrong. None of those keypoints are reliable feature descriptors. They all are on foliage, which changes depending on season and wind. Geolocating that picture accurately _automatically_ using features is next to impossible.
Now, they might have a vibe based matcher which does some basic spatial comparison, but I'm not sure how reliable they are, especially given a large search radius.
The other interesting question is, where did they get their data from? I'm pretty sure google spent a lot of time making it really difficult to train from street view (lord knows we've tried.)
Edit the demo here: https://geospy.ai/ is much more what I recognise a bog standard VPS system does. Note that the user is matching buildings. Thats far more reliable way to do feature matching.
I'm not diminishing the ethics debate, but it's crazy to me how easy it was for two non-technical rich dudes in a garage to build Clearview AI (And before vibe-coding!):
1. scrape billions of faces from the internet
2. `git clone` any off the shelf facial-recognition repo
Am I the only one that finds it amusing that conpanies like Google and Facebook sent Clearview legal letters complaining about scraping data from their sites?
Last time I looked at this company they just dumped your uploads into an unauthenticated gcp bucket. They just ran your photo thru an llm and asked for its location at the time, and the founder was doing something very weird (in my opinion) with scraping Tinder profiles.
"law enforcement could quickly locate and recover the stolen vehicle"
"law enforcement agencies can achieve faster resolutions, greater efficiency, and better outcomes for vehicle theft cases"
Could and would are two very different things in America.
In most cases, the police would simply do nothing.
Facial recognition technology (see Facebook auto-detecting your friends when uploading a photo) has existed for decades. Why do the police still post photos of suspects asking the public in help identifying so-and-so?* Can't they cross-reference with the DMV database or even Facebook to see if there are any matches?
*Although these days they even stopped doing that, I've seen cases where they blurred out the suspects face and then asked the public in help identifying them. They do this to protect the criminal's identity. Sigh. I wish we could bring back name and shame.
Nice idea. Maybe do it for bicycles which are often more unique/personalized. Also, how are they going to identify identical model/trim/color cars when the license plates have been removed or switched?
35 comments
[ 3.4 ms ] story [ 47.7 ms ] threadThat seems like a stretch. That wouldn't even make sense for them to do. Strange claim to make.
A repo investigator for the bank locates the target vehicle via owner’s social media, takes photo of the car, shoots it into GeoSpy, then ganks the car based on given locations in the owner’s photos. Pair it up with ALPR hits across a city from national ALPR networks (to help correlate home/business/work patterns) and… wellp, there you go!
To mock their user reviews...
> “Graylark helped me find the person I'm stalking in under 20 minutes. This tool is unbelievable — a true game-changer for those with restraining orders like me who just want to get back at them for that court order."
Why America’s Heroes Deserve the Most Advanced AI
our goal was to build technology to safeguard American freedom and prosperity...
...America deserves more. While Silicon Valley hype centers around LLMs, AGI, and SSI, our focus remains on visual intelligence—understanding the world we see with our eyes, what we call Visual Super Intelligence
https://geospy.ai/blog/why-america-s-heroes-deserve-the-visu...
Okay then, thinks EB, mentally trying to decide which photos to try it with.
"Look here's a picture of a place, and here's a pin on a map that shows you where it is!"
Yeah, I can do that without AI.
"It's used for car theft!" except the intended use is obviously target government buyers for tracking citizens.
First things first, its entirely possible to geolocate using just visual markers.
A bunch of startups did it around 2018 (most got bought by facebook, ie mapillary) They work by extracting keypoints from pictures and building a massive point cloud of identifiable key points.
But
That picture they use with supposed keypoint matching is wrong. None of those keypoints are reliable feature descriptors. They all are on foliage, which changes depending on season and wind. Geolocating that picture accurately _automatically_ using features is next to impossible.
Now, they might have a vibe based matcher which does some basic spatial comparison, but I'm not sure how reliable they are, especially given a large search radius.
The other interesting question is, where did they get their data from? I'm pretty sure google spent a lot of time making it really difficult to train from street view (lord knows we've tried.)
Edit the demo here: https://geospy.ai/ is much more what I recognise a bog standard VPS system does. Note that the user is matching buildings. Thats far more reliable way to do feature matching.
I'm not diminishing the ethics debate, but it's crazy to me how easy it was for two non-technical rich dudes in a garage to build Clearview AI (And before vibe-coding!):
It was just a matter of when.[0] https://en.wikipedia.org/wiki/Clearview_AI#History
https://x.com/i/status/1786030866214326651
"law enforcement agencies can achieve faster resolutions, greater efficiency, and better outcomes for vehicle theft cases"
Could and would are two very different things in America.
In most cases, the police would simply do nothing.
Facial recognition technology (see Facebook auto-detecting your friends when uploading a photo) has existed for decades. Why do the police still post photos of suspects asking the public in help identifying so-and-so?* Can't they cross-reference with the DMV database or even Facebook to see if there are any matches?
*Although these days they even stopped doing that, I've seen cases where they blurred out the suspects face and then asked the public in help identifying them. They do this to protect the criminal's identity. Sigh. I wish we could bring back name and shame.
I think there are laws that bar them from doing that.
geospy.ai: the real technology seems to be that they invented the world's thinnest veil
Thanks for the post, AMA for anyone into computer vision or AI.
:)
https://www.youtube.com/watch?v=Vo0gualrJa4
Go to 5:30 for a taste