Text OCR is the cannonical neural nets challenge (MNIST) so it seems natural to extend it to be something practical like LPR, without the insane difficulty of other real world shape recognition problems.

The easy ubiquity of LPR is essentially the death knell of privacy of movement. We'd have to move to encrypted transponders that only respond to queries with the right codes, but of course the police would still be able to know where you were whenever they liked. Rolling QR code digital number plates would work. But it won't happen.

Yes, what I found interesting the growth in articles discussing the technology and even open-source projects for LPRs. It's like vehicle facial recognition. Because of this, I've always been intrigued by subversive/defense techniques that fit into the realm of ML - like adversarial patches. Essentially, surveillance in some form is becoming ubiquitous, and private companies especially in the US will probably lead the way. It seems necessary to keep these systems in check at the citizen/individual level because political systems are out-paced by the rate of technological developments.

I like the "Rolling QR code digital number plates" concept; are you aware of rolling QR codes used elsewhere?

Recognizing individual vehicles is actually really difficult from a distance, because all the features are small -- scratches and dints at the 0.1-50mm scale, mm scale paint irregularities visible in other wavelenghts, misalignment of panels, window stickers etc. So licence plates are a huge reduction in privacy.

Other digital transponders, either for tolling or just your devices broadcasting their Wifi/Bluetooth addresses, certainly cause problems but they are within your scope to control.

It would be interesting to see if you could design an adversarial LPR jammer, that would not look to a cop like a licence plate.

When I wrote 'rolling code' I was being a bit off the cuff.

A PKI based system might be something like: {nonce,E(rego_pub,(nonce,car ID, date, hour)),S(car_priv,(nonce,car ID, date, hour))}, where rego_pub is the public key of the local authority, car_priv is the private key of the car, E is encrypt, S is sign.

This is already quite complicated, requires central PKI etc. And unfortunately QR codes themselves are not a great fit, because you need to be close to read them, but you could have a variant designed for shorter strings.

Another possibility might be to use a (prefix, TOTP code). With a fixed prefix indexed on car model/year (which is already visible from looking at it), the set of all TOTP codes from that fleet at that time could easily be searched by the registration authority to identify which vehicle it was. So, e.g., all blue Toyota Camry 2018s would have prefix 'P9J' and then a 10 minute changing code, like 'Y3KE'.

So if a cop needs to look up a car the LPR reads P9JY3KE. The local terminal says it should be a blue Camry, and then sends the string to the validator, which computes the TOTP for all vehicles in the class (like, 2000 in a group) and see if any of them match. If none match, pull them over.

There are a few problems with this still, e.g. replay attacks. But possibly solved by using IFF techniques, i.e. the police car can send a signed query forcing the named plate to show more digits of the TOTP code.

Criminals just steal the plates off another vehicle (of the same model/color if they are smarter), so maybe they can just steal the whole digital licence plate computer module? Unless the smart licence plate is part of the vehicle security system, on the car bus, it is still going to happen.

--- The whole IOV (Internet of Vehicles), V2V (Vehicle to Vehicle) space is going to have to deal with these problems, and there is no shortage of protocols but I don't think it is remotely solved. Lots of companies pushing 5G approaches for this, but I wonder if transponders like for aircraft (ADS-B) or ships (AIS) won't be a simpler (and much cheaper) way, even if they are still using mm wave radios.

Police use cases are driven by federal grants to local law enforcement, and the companies tend to be smallish, product driven (vs service) and not great from a technology integration perspective.

Ditto for similar big systems like EZPass are expensive. Some read transactions cost the government as much as $2.

It’s a market where the price is going to plummet as it becomes a service delivered thing. You’ll pull into a grocery store parking lot and generate an alarm to have a clerk get your groceries ready, and when you park it will tell him where to go. You’re also going to have a lot more tolls.

For what it is worth, I'm pretty sure that Amazon is already doing this at their Amazon Fresh drive-up stores. When you drive up, a sign lights up to say that your order is coming soon. The experience degrades a little bit if you arrive in a car they've never seen before -- a human stops by your window with a tablet to check you in.

That’s really cool!

Haven’t seen these yet, but Target has a pretty cool system that is app based. Easy to see how they could hook in cars.

Huh. It didn't even occur to me that they could also be using the app on my phone to do the same thing, and do it more accurately.

I've been mulling over trying to build one for our cove/neighborhood.

Ring/security cameras are fine but being able to pinpoint a handful of plates to the wee hours of the morning would allow theft issues to be handled pretty easily when cross referenced with camera tech.

Ex. Right now you get a description like "Older white 4 door" or "White Nissan Sentra"

Bounce that against plates from that time and you've got a good match to work off of, especially if the owner matches descriptions from cameras.

You don't need automated license plate recognition for that, though. If you find the motion events in the wee hours, and have a sufficiently high-quality video frame [1], you can easily check the type of car and then know the license plate by reading it with the neural network that was preinstalled between your ears rather than a software one.

I've been writing my own home surveillance camera network video recorder software, so I've thought about plugging in something like this, but I don't think it's really necessary. The one time I might use it is if there's a car that was likely involved in a burglary, I might scan the previous few weeks to find previous times they were in-frame. Then I might see their scoping the place out, potentially revealing more about themselves. It's too labor-intensive to look through that many motion events by hand.

[1] My understanding is this requires some care. You have to select the right kind of camera, place/aim/zoom it carefully, and tune it for license plate recognition, particularly at night. I think the cameras typically come tuned for slow-moving, not-very-reflective-to-IR objects. License plates on a moving car are the opposite, so you want to say decrease the aperture and increase the shutter speed from the default, position it carefully as I said, maybe tweak some other settings, and test it.

The danger of doing that systematically is that you have a record to subpoena.

So be prepared for the requests from divorce attorneys and police. Better to just have video to make it more difficult.

The primary idea behind this post is to show how you can use OCR technology to one of the simplest problems (LPR). Given how widespread Licence Plates are and how easily you can collect this data. Most other use cases are a little more niche and difficult to relate with.

I doubt LPR will be the primary utility of this kind of tech.

> I doubt LPR will be the primary utility of this kind of tech.

You're right. LPR isn't going to be the primary utility, but it's very possible that this will be one of the first areas to benefit from this tech at scale.

I wonder if openalpr is helping.

https://github.com/openalpr/openalpr

basically:

    alpr imagefile

will recognize a plate. It just as simple in python.