AI Technology will Alert on Robbery Signs

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Many robberies often go unpunished because they’re generally only reported after the perpetrator has fled unless someone is always watching the security cam — a costly service.

There are security cameras everywhere, and deep learning models are getting really good at spotting individual objects in footage — for example, masks and guns. So why not put the two together? – the founders of Deep Science AI thought.  

Deep Science AI aims at using the tireless analysis capability of AIs (artificial intelligence technologies) to augment human oversight to provide a cheaper and maybe even better way to keep an eye on your store.

“We’re the first to build a whole platform that can scale up to thousands of cameras, and the first to do a real-world deployment,” CEO Sean Huver told techcrunch.com.

A great deal of security cameras are IP-based, sending their footage to some data center to be archived or, if they pay for it, monitored. Deep Science’s system sits on that stream and runs it through a set of neural networks trained on thousands of hours of real robberies — and a few fake ones.

The resulting systems do pretty well down to about 30 “pixels on target,” meaning that the gun or face is most confidently detected when it’s that many pixels across in the footage. If one network isn’t quite sure whether something is a gun, the imagery gets sent to a “binary specialist” network that gives a second opinion.

If it looks bad enough, it goes to a human, as an alert in a Slack channel, who can alert the authorities.

“We’re at the point where one analyst can deal with about 500 cameras, and they deal with one false positive every 40 seconds or so,” Huver explained. They hope to get that number up to around 800 or 1,000 as the networks improve and the false positive rate drops.

But the company is confident that the system in its current state is more than good enough to deploy. In fact, it’s already deployed in a limited way.

It’s been running in a closed beta for four weeks at 18 locations, which has helped improve their algorithms.

Sunoco, which runs more than a few gas stations, has expressed interest in joining and will enjoy this cheap alternative that is as effective as human monitors.

Detecting masks and guns is a sort of minimum viable product, but the company doesn’t intend to stop there. Once they’re in the system, there are all kinds of interesting data that can be extracted. Perhaps there’s no gun or mask, but an employee puts their hands in the air. Or maybe a window is broken when no one is around. Or maybe a fire starts, and can be responded to even before the smoke detectors go off.