Why Security Needs an AI Device Category Now
Consideration of the hardware devices that are needed to make the most out of AI software advances often gets lost in the conversation.
Adobe Stock image by Ar_TH
The advent of artificial intelligence or AI is rapidly changing the realm of video surveillance software, turning what was once a live monitoring or post-event forensics tool into a predictive business enabler.
Whereas, in the past, organizations would need to manually analyze footage to identify trends, today data dashboards are available that provide up-to-the-minute updates on things like building occupancy and traffic volume.
However, what often gets lost in the conversation around the continued innovations that AI is bringing to the market is consideration of the hardware devices that are needed to make the most out of these software advances.
Despite the proliferation of surveillance cameras with embedded processors that offer the promise of running analytics at the edge, the fact is many of these devices can only run simple analytics, such as motion detection, as they lack the processing power necessary to perform more complex tasks like facial recognition.
As a result, in many instances, the end user will be required to deploy an onsite server that possesses the necessary horsepower to provide the information they are looking for. That often leaves customers with sticker shock, as some of these appliances can run into the tens or even hundreds of thousands of dollars, depending on the size of the deployment.
How Security Industry Can Respond to AI and Its Growth
To address this, we need a change of mindset in the industry. Not all analytics and the devices they run on are created equal. Rather than trying to fit a square peg into a round hole by deploying AI software solutions in environments that are not well-suited to support them, perhaps it is time that we start designing surveillance systems with an AI-first approach.
Think of it this way: You wouldn’t deploy analog cameras on a widespread basis in your facility if you wanted a full cloud surveillance system, so why try and take compute power heavy technologies like AI and analytics and run them on legacy infrastructure that was built for the traditional video management environments of old?
Perhaps our industry needs to have a conversation to establish a better understanding of this challenge for our customers. One area of discourse might be a new category for AI devices that have been purpose-built to support today’s software solutions.
Coming to market more and more are solutions in which the camera is built around the analytics design in order to deliver the most accurate information based on the task the solution is intended to perform.
Already common in under-vehicle inspection, pedestrian detection and lighting, purpose-built devices will become more prevalent in our industry. Unlike adding compute power to an edge camera, these holistic solutions are designed to simplify the deployment of AI and advanced analytics at a customer site.
Demand Will Continue to Increase
Demand for these and other types of solutions will likely continue to increase as end users realize that their existing infrastructures are incapable of delivering the video intelligence capabilities they need.
Eventually, a camera will not be enough, nor will an “AI box,” as customer needs in complex environments will lead to new concepts in manufacturing that integrate analytics into the overall solution. Manufacturers must be ready to support these demands, as well as educate the market on what is needed to ensure that customers get what they pay for.
Despite the buzz surrounding AI and what it portends for the future of security, many are still not leveraging the technology to its full potential because they do not have the required hardware, resulting in lost revenue opportunities for integrators and reduced return on investment for end users. Let’s start to change the industry narrative on AI by enabling people to get the most out of their investments.
Matt Powell is managing director for North America at Intelligent Security Systems (ISS) and a member of the Security Industry Association’s Artificial Intelligence Advisory Board.
The original version of this post appeared on SIA’s website and is used with permission.
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