Video Analytics: Ready For Prime Time?

The often-maligned technology is making strides


More than 40 years ago, the founder of Intel, Gordon Moore, first predicted that the number of transistors placed on an integrated circuit would double every two years — leading to regular, fairly predictable advancements in computer-related technology.

Moore’s Law has proven to be pretty accurate for processing speeds, memory capacity and the number and size of pixels in digital cameras. And it has held up well when discussing advancements in video analytics, a technology where computer-based algorithms are used to sort through video for noting people or vehicles in restricted areas, unusual behavior, object tracking, identification and counting, an abandoned bag in a train station or airport and a host of other tasks.

Nearly two years ago, my colleagues from SecurityNet and I were asked by STE to bench test 13 of the then-leading video analytic offerings on the market. What we found were largely expensive solutions — most often built into a DVR or stand-alone box — that needed frequent tweaking and were not ready for the mass market.

Since our tests in 2007, video analytic technology has changed, mostly for the better. The engineers have improved at writing programs that can distinguish between people and a stray dog or a tree blowing in the wind. Some of the offerings can now automatically tune and adjust themselves. And the cost per camera, or channel, is coming down. The technology may still not be quite ready for the mass market, but it is getting closer.

Moving to the Edge

One of the biggest changes I have seen is analytics moving from a stand-alone box to residing in the camera. That is the direction that always seemed to make the most sense. The analysis is done on the pixel level and the pixels start in the camera.

Using analytics in a box with analog cameras, end-users must transmit the video back to a central location, convert the data to digital format and then analyze it. With IP cameras, the video is transmitted to the station, uncompressed and then analyzed. Both of these scenarios take extra time and are not really efficient.

One reason analytics began its life sitting in boxes or on a DVR is the tremendous amount of computer power required to do the job of processing the images. But here is where Moore’s Law plays a role. Most current CCTV cameras are built around digital signal processors. DSP’s are also the tool of choice to compute the algorithms at the heart of all video analytics. As the power of these DSPs continue to grow exponentially, it has become possible — maybe even preferable — to add basic analytic functions into the camera.

Some of the big camera manufacturers are now building some fundamental, basic analytics into their products. Pretty soon, we will see the rest of the major camera manufacturers doing the same. They are going to be able to offer basic analytic functions for a couple of hundred dollars per channel.

The proprietary analytic box makers are generally charging anywhere from $1,000 to $3,000 per channel depending on the number and scope of the functions built into the box. There is a big difference in price and that does not bode well for the box makers. When you can lower the cost by a magnitude of this size, as the camera manufacturers have done, that is a big deal. That can change when and where people are willing to use analytics.

So, it is going to the edge. That writing has been on the wall for a while. It just takes the big guys — the camera manufacturers — a little longer to get their act together, but when they do, they will likely capture most of the market.

The IP cameras are going to have the power for analytics built into them, or at least the option to do so. This will work well for the end-user starting a new video system or expanding an existing one. Where a legacy system is involved, it may be best to replace analog cameras one at a time as they wear out with an IP model.

Thinking Inside and Outside the Box

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