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It's not often I get a chance to "get my hands dirty" with an emerging technology, so I jumped at the opportunity this morning to see one of the newest security product offerings in our industry. Milestone Systems, a Danish company that has created open platform video management systems, is hosting its Milestone Integration Platform Symposium in Puerto Rico this week, and I popped my head into a training class on their new XProtect analytics offering for license plate recognition (sometimes also called Automatic Number Plate Recognition, especially if you like fish and chips and your afternoon cup of tea).

First, a couple notes about this product - it's not officially launched yet, but Milestone is OK with you hearing about it (expect official launch fairly soon). This solution is a tie-in from a company called Dacolian out of Holland. This is Milestone's first real foray at offering a direct-embedded "analytics" system (license plate recognition is most definitely part of "analytics", even though most people think of analytics as virtual trip wires and abandoned object detection). The company is, of course, still strongly partnering with some of the big names in analytics, and that's clear with some familiar faces from Vidient, ObjectVideo and Cernium presenting here at the Milestone symposium. Those companies and others can do a lot more than identify license plates, so it's not really a competitive squabble, in my humble opinion. Nonetheless, it looks like this license plate recognition software add-on is the first in a line of analytics products that would be available directly from Milestone.

So let's get back to when I was getting my hands dirty, courtesy of the Milestone tech guys Morten Lundberg and Rasmus Lund. The Milestone license plate recognition system essentially takes a stream of video (in M-JPEG format) and splits it into a series of images (pretty easy to do, considering this is Motion JPEG). The system then sends that license plate data through the license plate recognition (LPR) administrator program where it identifies plates. The LPR unit then sends that plate info to XProtect Transact's server for storage as metadata.

That's right, XProtect Transact - a Milestone solution that I had thought of as only designed for retail environments - is at its core a system for linking metadata with video. That metadata could be point-of-sale information (as it is typically used in retail environments), but in this case it's the character sequences of license plates. The system requires users to have XProtect Enterprise or XProtect Corporate software (XProtect basic isn't going to cover you here), but it's not particularly processing intensive; in fact, I was running the whole system (XProtect Enterprise, Transact and the LPR add-on) on a rented Lenovo laptop. Admittedly, that's probably not the preferred way of using processing power if you have a number of cameras, but it worked for the purposes of a simple demonstration.

As you might expect, when setting up license plate recognition in the Milestone/Dacolian system, you're able to make a number of configurations. You can set the accuracy threshold (I set a minimum of 60% confidence, but most of my matches were in the mid-90s percentile, and none were below 85% in this small test case). What that does is tell a system that if you've got less than your minimum confidence factor, it doesn't mark that as a recognized plate in the system. Of course, you're still recording the video so you can manually go back and review video where no plate was indicated, but it seems the goal here is to not dump in a bunch of plate recognition "guesses" if, perhaps, you're dealing with a license plate that's been through a mud-wrestling match or is covered with that cold, white stuff we call snow. And while I'm here, I should note the maxim that Rasmus offered: "When we speak of analytics, 100 percent certainty is more theoretical than practical." Consider those as words to live by if you are contemplating analytics.

To match a plate and determine the confidence of the match, the system relies on a number of pre-defined license plate styles for countries around the world and U.S. states. Some of these states have many (sometimes 30) pre-defined templates. That's designed to cover the specialty vanity plates being offered today, and it sounds like Dacolian is staying current with that and trying to add more and more of these bizarre plates that state tax departments are selling as revenue boosters. It's not just the U.S. that is plagued by specialty plates; Morten tells us that in his home country of Denmark, where the total population is 5 million (about the size of the metro Atlanta) they have 42 different types of license plates.

In my example for the demo purposes, I had a number of photocopies of how Texas license plates look on American cars. The blurry photocopies were about as good a testing ground as we could create in the class setting, shy of actually setting up a beta test in front of the hotel.