Going My Way

Expectations for AI go far beyond what you might think

• By Thomas Cook
• Feb 01, 2020

Artificial Intelligence (AI) continues to be a hot topic in the security industry. While true AI (a computer’s ability to think and act like a human) is still decades away, subsets of AI such as deep learning and machine learning have enabled computers to make significant strides, enabling machines to examine large amounts of data to provide deeper insights. So, while we do not find ourselves in the realm of true artificial intelligence, using the term AI to describe any aspect of it, seems here to stay.

As more “AI-based” cameras come to market, it is interesting to hear how end users think about deploying the technology. Recently, I participated in an end-user conference attended by a diverse group of Fortune 500 organizations along with government and transportation groups.

We asked them what they wanted from AI-based cameras in the future and the expectations expressed were far beyond what you might expect. The promise of AI has infiltrated almost every corner of our business and personal lives. The prevailing wisdom is that AI has the potential to do anything, so in addition to advancing their security capabilities, customers are looking to AI for help in solving their operations and business challenges as well.

Further Expectations

This trend is bolstered by consumer products and services that continue to push the envelope too, setting further expectations for AI for products we use in our day-to-day lives. For example, if you buy a new car today, there are sensors everywhere which can inform you if an obstacle is approaching as you reverse, slow you down automatically when you come up behind another car or subtly push you back in the lane when you drift out.

Autonomous vehicles have put AI front and center as they use this technology to enable cars to drive themselves. These ideas of computer assistance are becoming a common expression of AI across all of our devices.

It is a challenging part of the future, whether we like it or not, and security systems will no doubt be following the same path. It is easy to imagine smart systems making decisions with little to no human intervention—automating reports and sending them to police stations, end users, directors and IT departments without anyone having to directly interact with it.

Because AI is marketed everywhere, it is not surprising that end users expect much more than what the industry currently offers them. While discussing the fact that AI-based cameras can recognize the color of a person’s shirt and pants, one of our customers said, “Yes, that’s useful, however, we would like to be able to detect our company logo on shirts. When an employee enters a camera’s field of view, we want to know if they are wearing the appropriate company attire and are therefore compliant with standards.”

Even before customers receive their first AI-based cameras, they are evolving their expectations of the technology to fit their needs. Another example came from a food processing customer that wanted their cameras to read labels on packages of meat to determine if they are nearing the expiration date.

Today, while we might be able to identify a piece of chicken, we currently have no way to tell if it is expiring. Perhaps we can partner with another sensor company to scan packaged meat items when they pass through a distribution center and satisfy the customer’s request, but you can see where this is going. Likewise, a major pizza company asked if they could see if the pizza is placed correctly in the box before it is shut to make sure the toppings stay on the pizza rather than sticking to the box. Obviously, it is a part of their process they want to improve.

Opportunities for Business and Operations Intelligence Are Growing

The security industry, which until recently was seen as a commodity, now has the opportunity to reinvent itself as cameras and supporting infrastructure also become smart devices. There is plenty being done on the mechanical side, but also on the electronic video side. People recognize the advancements and the innovation possible at the camera level as the devices become increasingly smarter.

Having more intelligence at the edge gives end users a dramatic advantage to process data and makes changes in real time. In the past, we would have to take the data and pass it on to servers at the head end, or in NVRs, but we are getting to the point now where the valuable data we mine can be shared from cameras directly to additional systems and services allowing business to do more with it.

Partners can aggregate the data into charts and graphs while linking into POS systems as well as intrusion and facial recognition systems. We can take all of this valuable information from the edge and then disseminate it where it is needed.

Our most recent end-user conference gave us a tremendous amount of feedback, but while the sky may be the limit in the customer’s mind, it is important to set expectations accordingly when it comes to uses of AI. In some conversations, it was clear that end users are assuming they can teach AI-based systems themselves without realizing just how complex an endeavor that can be.

For a deep-learning algorithm to correctly recognize an object, it has to have been shown that object hundreds of thousands of times (if not much more) in varying environmental conditions. When the algorithm gets something wrong, it has to be corrected and tested again. While this technology might be here before we know it, machine learning and deep learning are best left to experts in the technology, and if we want to keep things 100 percent accurate, it is important for people to know that training an algorithm is a long way from teaching your smart speaker or phone to recognize your voice.

The Age of Analytics

Traditional motion analytics are pixel-based, and depending on the environment in which they are used, can easily generate false positives. For example, if a bag is left in an airport, and an operator is doing forensic research to find out when the bag was moved, a pixel-based motion analytic cannot discern the difference between the item physically being removed or if a person is standing in front of the bag and blocking it from the camera’s view.

So, while these analytics are useful, there are many instances where false-positive data is likely to occur. Likewise, real-time alerts from video security systems provide an excellent line of defense, but the susceptibility to generate false alarms in the past, such as wind blowing trees, have caused many organizations to pull back on all but the most guaranteed scenarios.

False-positive alerts can be costly for a company. We have lived with technology in the previous decade that was not 100 percent accurate, and instead of losing credibility within the organization, many security teams have opted for the safer route which was to avoid false alarms at all costs. The challenge now is to demonstrate to end users and the entire industry that the technology has improved so significantly, thanks to deep learning, that it is reliable and can be trusted.

We are going to see more AI-assisted products coming out in 2020. Cameras with deep-learning technology can recognize programmed objects and describe unique characteristics such as color and worn accessories. They can identify a person with or without glasses. They can detect whether you are holding a cell phone to your ear. This represents an exponential improvement over pixelbased motion analytics in their ability to prevent false alarms.

It is All About the Data

AI is gaining momentum every day, partly because it is poised to infiltrate every aspect of business and our personal lives. Ultimately, it is all about organizing and making sense of data. Modern cameras can gather much more than video image data. They can also classify sounds, recognize and count objects, display heat maps and know when tampering occurs.

They are an important data gathering tool that is already well accepted and commonplace in our world. This continues to move them well beyond their original commodity status and into a revenue-generating device that can provide multiple services for business intelligence applications as well as operations and process measuring and metrics.

The key takeaway here is that customers, when asked what they want to see from AI technology, did not reply with security workflow improvements (possibly because they do not know what is achievable yet). Instead, many replied with operations and processing needs. However, there is a clear desire for smart cameras to assist more in business operations.

Let me give you one final example: A large airport expressed interest in having cameras installed at every gate so that staff could be alerted when an aircraft arrives at the gate. Currently, the only way they confirm this is to have a person physically look out the window.

This ties back to security too, of course, knowing whether the door should be opened or not. AI-based technology is certainly going to bring exciting change and plenty of challenges to our industry in the years ahead.

This article originally appeared in the January / February 2020 issue of Security Today.