465
Flock misread license plates in 71% of the alerts it sent to police in one California town
(www.businessinsider.com)
this post was submitted on 31 Jul 2026
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How can they suck that bad? A 29€ raspberry pi with a basic ML model can get 90% accurate results
I guess that they are implementing crappy cameras? Not sure about american plates, but in most jurisdictions licence plates are optimized for easy OCR:
Note how each letter is unique and can't be transformed with a sharpie into another. That also makes OCR easy. If you use a crappy dashcam, you will get problems regardless. Esp. in low light situations at higher speeds.
So I would guess that Flock uses cheap sensors and cheap lenses. Which kind of makes sense when they are putting out thousands of scanners.
In some cases in the US, cops enter both O and 0 into the search databases. This probably compounds the number of false positives.
Are they? Off the top of my head Germany is the only country I can think of that uses such a font (FE-Schrift).
German wiki mentiones 42 countries that also adapted the FE Schrift and 7 that use some adaptions:
https://de.wikipedia.org/wiki/FE-Schrift
I see. I'm in Europe and it's not very common there so that's why.
in my area I struggle to distinguish D, O, and 0. Idk if someone was paid to design that, but they did an awful job
Never seen letters like that on license plates in Quebec.
That's probably exactly what they did, deployed a sbc + some off the shelf model and increased the price 300x by making a black plastic shell.
If you have a pi reading license plates, you are probably scanning a few dozen different plates a day, looking if they match a handful of possibilities. If the only plate that opens the gate is ABC123, nothing happens if it accidentally reads anything else, and I'll probably get it right a second later.
When Flock scans plates, it reads thousand of them every second looking to read and match every possible plate variation from every state (which there are over 8000 of) to every reported stolen plate, in all weather and lighting conditions. It's really no wonder if 71% of the times it reports it found a stolen plate it had got it wrong.
I highly doubt a single flock camera would scan thousands of plates per second lol
There is no good reason for sending images of plates to the cloud for alpr when the camera can do it locally
I did not know there was only one single Flock camera in the city of 150 000 people, or in California, or the USA in general. lol.
Oh, kinda looks like they have more. 20 billion reads a month, huh. That's 7610 scan a second. Fancy that.
I don't think you understand their point. The point is that the OCR can be done on a distributed system, because a distributed system is what the cameras already are.
My biggest contention with this claim is whether the computers onboard the license plate readers are actually capable of doing this with solar power and battery capacity, but considering I'd rather they didn't exist anyway...
Flock cameras process the images locally for plate recognition. It’s only after the image of the plate has been converted into text that the plate number (or in this instance the number it has misinterpreted) is sent to AWS for matches and alerts. The issue sounds like it’s at the individual camera level.
...yes?
The cameras around the US scan 7000 plates per second, trying to figure out what they say with 8000 different possible plate designs around the country, and those results are then matched to a database of stolen vehicles and other plates , which there's roughly 600k reported stolen in the US each year so. That database is going to be massive.
That means every second, 7000 strings of letters and numbers OCR'd at unknown conditions and accuracy are being compared to hundreds of thousands of stolen plates. They are guaranteed to get a number of "matches" constantly, most of them from reads that were not accurate.
And specifically because they aren't sent anywhere, nobody can double check the picture to see if the camera got it anywhere right at all.
Yes the entire system saving that text, and maybe a still image handles that many... But the single camera taking each photo where the actual OCR processing is done ON THE CAMERA HARDWARE ITSELF only needs to handle maybe one or two each second... And only if it is looking at a busy road. At the distances these cameras can process, most are not handling a lot simultaneously. And very busy roads will have different hardware than the lower end camera systems used in most places.
You HAVE to purposely be trying NOT to understand any of the responses to you here. No one could possibly be that stupid unintentionally.
Holy fuck you're daft, good luck
What does any of this have to do with the cameras from one specific town being wrong a majority of the time? Not that it would be better if it wad the average across the nation. It would be much, much worse actually.