Eyeballs at the Edge

Eric Broockman Chief Technology Officer Published 13 Nov 2019

In my position, I meet lots of great technologists, from semiconductor practitioners to cloud application developers, from IT staff of small cities to CTOs of some of our most successful channel partners as well as execs from our largest customers. Frequently I get asked, what is one of the newest technologies we should be paying attention to? I often answer with – “Watch for the coming era of eyeballs at the edge“.

What does this really mean? It isn’t about the profusion of video cameras that continue to crop up in buildings or on campuses all around our cities. Most of those are related to physical security. Rather, the technology inflection point and trend I see hurtling towards the enterprise and becoming more real every day is video analytics at the edge. So why call it eyeballs at the edge? When you look at the various use cases, many of them are an attempt to apply what by human standards are essentially relatively simple visual, think eyeball, image recognition capabilities to a video stream source coming from a digital video camera. What is especially interesting however, is that the chips and other infrastructure that enable this capability are undergoing an inflection point in price-performance, size, throughput, and power consumption. What was thought of not long ago as a huge CPU hog, that then became a super high-power specialty version of a GPU chip, is now becoming a far more modest chip, or collection of chips, attached to PCI or USB connections that cost less, are much smaller and consume far lower power.

But how is this eyeballs at the edge? Let’s cover a few examples. In healthcare, a surprising amount of time is spent by very important staff simply looking for expensive portable medical equipment. Eyeballs on the lookout. Think mobile X-ray machines or ultrasounds or infusion pumps, etc. A great deal of money has been spent by institutions using various forms of wireless technology to help find these high-value assets. They help, but their precision can be suspect. Though the future of UWB locationing holds great promise, another way to look for stuff is to look for stuff with eyeballs at the edge. In particular, using video analytics to identify important objects as they move down hallways in health clinics – just like a human eyeball does. Video analytics can be trained to easily recognize any number of important high-value assets in a healthcare setting. This capability enables new asset location applications to identify that the mobile X-ray machine that used to be in room 217-B, traveled down the hallway and can now be found in room 263. Note that this application is a true edge application for a few reasons. In some instances, being able to give relatively immediate real-time asset location information precludes using a distant cloud to perform video recognition and analytics. In addition, the sheer volume of data, even if it is already compressed, that would be sent to a cloud from 100s of video cameras in a modern healthcare facility would generate an exorbitant cost in simply transporting the bits to the cloud. Taken together, this drives the need for edge computing. The newest technology coming to market makes this possibility remarkably affordable versus just a few short years ago.

Perhaps another example. Today, in many stadiums there are already people counting “eyeballs at the edge” applications, keeping track of queues at bathrooms, etc which then trigger electronic signage or alert venue engagement applications on your smartphone when the queue times become uncomfortably long. A more sophisticated use case, however, is sentiment analysis in a retail environment. If you were part of a retail digital engagement team, wouldn’t it be useful to be able to tell when a customer standing in front of an item on display was interested, disinterested, curious, needed help, etc? Video analytics can help here. The algorithms are getting good enough to detect more than simple happiness or unhappiness. They can give insights into whether a customer is looking for help – much like a human associate can tell by simply watching the facial expressions of a customer. “Can I help you? It appears you are a bit perplexed by this product demo.”

Get the latest stories sent straight to your inbox!

What about a higher education example. Video analytics are getting good enough to tell the difference between a group of friends walking home from a game at the stadium on campus and celebrating, and two people in the midst of a fistfight – or worse. What about license plate recognition in cities to assist traffic improvement software in a smart city?

As the algorithms get more advanced, the true power of eyeballs at the edge becomes ever more real, and the usefulness greatly expands as well. There are certainly some Orwellian creepy use cases that can be conceived of, but like any new technology, the potential for the greater good is high. So why is this trend about to suddenly become a “thing” you might ask. Like any inflection point, it is usually because of a step function change incapability of some underlying technology. The step function here has not been the change from X86CPU to an Nvidia GPU, though that was a step that greatly accelerated the field. Rather, from my vantage point, it is the newer inflection point of custom video AI accelerator chips from companies like Intel with their Movidius chip, or Google and their small TPU chip, or new startups like Mythic with their promised and forthcoming unique mixed digital and analog approach. These chips enable the capabilities of far-superior price/performance/watt solutions that may be delivered in an edge computing server, or be part of a smaller form factor, ruggedized Intel NUC-like form factor or the new video cameras coming to market with embedded video AI, and even Razor’s Edge computing models where the video accelerators are blades powered by PoE directly attached to wiring closet switching systems. When these types of step functions – inflection points – happen, following the ideation gestation period, there is a plethora of new applications, which begin to grow rapidly, cross the proverbial chasm, and become part of our mainstream way of life. Eyeballs at the edge is one of these promising and forthcoming inflection points to keep your eyes on.

This blog was originally posted on VMBlog on November 12, 2019.

Related Enterprise Stories