Video Analytics Makes IP Cams More Accurate, Competitive

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Competition in the DIY home video surveillance market these days is fierce. Five or six years ago, there were two products for homeowners who wanted cost-effective video monitoring that they could access remotely from a computer or smart device. Today, there are at least thirty, and counting.

In that kind of crowded marketplace, one way to elbow your way to the front of the pack is by being more accurate than everyone else.

What does it mean to be accurate?

Well, consider that many if not most of the DIY home monitoring products available today use some sort of basic motion detection to send alerts to customers’ devices, notifying them that the camera has detected activity. Many systems detect even the slightest change from frame to frame in the camera’s field of view, and send an alert to the customer each time.

If the customer happens to live in a vacuum, where no pets walk through the room, and no curtains flutter in the breeze, and no shadows move across the walls, that system works fine. If they don’t, their phone is going to be filled to overflowing with notifications. After a while they’re just going to ignore the alerts, or turn them off altogether.

A lot of false alerts means that a system is inaccurate. Inaccuracy is especially disastrous if the primary purpose of the surveillance is security. Accurate, real-time alert functionality is the cornerstone of do-it-yourself monitoring.

The important thing to note here is that even the best camera in the world, if it’s equipped only with basic motion detection, will provide a frustrating user experience with respect to alerts. To be competitive today, an IP surveillance camera needs video analytics.

While video analytics definitely improves alert accuracy, it’s important to note that not all video analytics software is created equal.

There is, in fact, a wide spectrum of video analytics available on the various DIY home monitoring products you can purchase today. There’s basic motion detection with sensitivity control (see our article on why adjusting the sensitivity of basic motion detection is a useless activity); “pet immunity”; custom “zones”; zones plus high-tech outdoor filters for shadows, light, weather, etc; face detection/recognition; and all of that plus the ability to learn customer preferences.

Essentially, the further along the spectrum you go, the more accurately the system can alert users to and record important events (and filter out meaningless ones), and the better the user experience is. It simply comes down to the technology doing what users want it to and not wasting their time.

With the advent of accessibly-priced outdoor and weatherproof cameras, video analytics has become even more critical. Sophisticated analytics can now filter out rain and snow, plants moving in the breeze, and even moving shadows on an overcast day, making it possible to have motion alerts on cameras monitoring outdoors.

Outdoor or weatherproof cams without sufficient technology to meet the significant challenges of outdoor scenes will simply be doomed to function like old-fashioned CCTV cameras—good only for live streaming or 24/7 recording. That’s a waste of an outdoor IP camera’s potential. (Not to mention that 24/7 recording and streaming comes with its own set of problems, such as the strain on the network bandwidth, and the question of where to store the video footage.)

At the very end of the analytics spectrum is the ability of the software to learn. Learning brings accuracy to the next level. In user-defined learning, the customer tells the system what is important or unimportant, and the system learns and applies that to future events. For example, a customer may have drawn a zone exactly where they want it, but occasionally when the wind is strong, a flag flutters into the corner of the zone. A system that learns can be taught to ignore the waving flag, thereby improving the accuracy, and consequently the customer’s mood.

 

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