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Using Deep Learning to Make Video Surveillance Smarter

Startup Camio is drawing on neural networks to better identify who—or what—is outside your door.
August 17, 2015

A startup is making home video monitoring smart enough to figure out whether a dog, cat, or package is heading up your walkway.

Camio, which offers an app that lets a smartphone or tablet act as a surveillance camera and works with some individual cameras, already uses machine learning to point out the most significant events captured by a user’s camera that day and to let users search for vehicles and passersby as they come and go.

But starting this week, Camio is expanding its use of artificial neural networks—a machine-learning technique that draws on the way networks of neurons in the brain adapt to new information—to enable users to search their recordings for several trickier-to-identify objects like cats, dogs, bikes, trucks, and packages.

People can also set up alerts to know when these things have been captured on film, and an integration with the online response-triggering service IFTTT (“If This, Then That”) will let them set up preprogrammed actions that are put into motion by what the Camio-connected camera sees. (For instance, you could order it to call your smartphone when a cat approaches your door between 2 a.m. and 6 a.m.)

Camio cofounder and CEO Carter Maslan says the new object labels and IFTTT integration are expected to be available on Tuesday, and that Camio will add more searchable terms over time.

“The key is in finding a threshold where it’s precise enough,” Maslan says.

While some other consumer surveillance cameras like Nest Cam can send users alerts based on motion, sound, and face detection, the use of deep learning could lead to much more nuanced observations of what’s being picked up by a camera lens.

Camio, based in San Mateo, California, determines that something being recorded by a user’s camera is interesting by detecting a significant amount of motion in a scene. Maslan says the company currently uses neural networks with each video camera that concurrently vote on what they think the user would consider interesting. The technology is proved right or wrong based on videos the user eventually opens, plays, and deletes. Users can help the system learn by giving clips a thumbs up or down.

Maslan says the heaviest computational work—and therefore the most expensive—involves figuring out exactly what’s happening within the bits of video that are determined to be interesting. Typically, he says, this is about a minute’s worth of footage each day, so Camio is just using neural networks to further analyze that bit of video, rather than slogging through what was shot over the whole day.

“Instead of hours and hours of recordings, you can ultimately not even watch the video and just get the information you need,” he says.

It won’t be free, though. Until this week Camio, allowed users to stream and store a month’s worth of video from a single camera without paying; starting this week, Maslan says, the company will continue letting users stream for free, but it will charge $9 per camera per month to record and play back video.

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