Artificial intelligence (AI)-based technology seems poised to enhance nearly every aspect of our lives. The security industry is no exception where we see AI-based object detection and classification providing analytics with increasingly reliable data. One of the chief benefits is the reduction of false alarms, and the ability to finally reap the benefits analytics have promised for security and business intelligence.
The Trouble with False Alarms
False alarms cause onerous interruptions in the day-to-day operations of enterprises while also creating material inefficiencies within monitoring centers. For the enterprise, such as a chain restaurant or retailer, a false alarm often means having to drive to the location late at night or in the early morning hours to confirm what indeed triggered a burglar detection system. Where police dispatch occurs unnecessarily, businesses can be slapped with substantial fines. At best, store operators must deal with unnecessary after-hours calls or texts from their intrusion alarm provider.
Significant gains in alarm verification have been made by integrating on-premises video with alarm systems, allowing monitoring centers to receive live and recorded video in conjunction with a burglar or panic alarm. Despite these considerable advances, video verification is dependent on a live operator’s decision-making ability, along with the human eye and the inherent flaws of each. Video verification is particularly challenged in outdoor environments where traditional alarm triggers, such as motion detection, are too numerous to process. Wind, animals, and minor changes in lighting can keep a conventional motion detector so busy as to be ineffective.
AI can take the current alarm monitoring operation to an entirely different level of accuracy and efficiency. AI-based deep learning algorithms integrated with cameras can now detect people (human motion) and filter out all other motion like shadows and trees as well as animals and traffic. This is a game-changer for live monitoring operations as operators can now rely on motion detection to alert them to people in areas where they should not be. With so many camera feeds to be monitored, AI-based analytics effectively act as a second set of eyes that never sleep and never miss an event. This guarantees that live monitoring operators are not wasting their attention on false alarms.
AI-based Exception Reporting
Some solution providers are starting to add machine learning into their exception reporting platforms. When you expose thousands or even millions of sales transactions to a machine learning algorithm, it can begin to recognize patterns; patterns that lead to fraud, patterns that lead to theft at the POS. And when you add video to an exception reporting solution, we get to an entirely new level of intelligence. We might discover that not only is something problematic going on at the POS but maybe there’s no customer there at all. The power of AI allows us to recognize these patterns and alert a live monitoring operation to potential problems that would otherwise require focused research potentially involving hundreds of people hours. The response can be anything from an operator calling the location and letting them know something was triggered to the activation of an interactive “virtual guard” system where a live announcement is made through ceiling-mounted speakers. Informing individuals at the point of sale that there’s a security presence and the location is being monitored can have a sizable impact on shrink.
Although the legal landscape is far from settled, facial recognition is another AI-based filter that monitoring operations are beginning to use to protect businesses. It can help monitoring center operators answer questions such as, “Is this person authorized to be in this facility?” or “Is this person a potential danger to the business?” Privacy and legal issues aside, biometric recognition technology is not likely to go away. I’m confident it will be better regulated and more widely accepted over time (like our phones that grant access to all our data via our faces). At some point, monitoring operations will more aggressively leverage facial recognition solutions to deal with shoplifters and organized retail crime. If someone is a known shoplifter with a criminal record, a business justifiably wants to know when that individual walks into a location. If they’ve shopped at that location before, it’s reasonable to expect a security team to use an AI-based facial recognition tool to alert a live monitoring center so they can notify the store. They might even conduct some type of intervention over an audio system to let the individual know that there is a security presence and that they are being watched. On the flip side, the prospect of using facial recognition to recognize VIP customers is a powerful way to ensure that those individuals get the best possible service. For example, when a ‘whale’ gambler walks into a Vegas casino unannounced, the operations team wants to know so they can roll out the red carpet and ensure they have the best possible experience, lest they leave and spend their money elsewhere. Opting into this type of VIP treatment would be at the discretion of the VIP, but it’s hard not to think they wouldn’t want the special attention.
Deliver Value and Power
When it comes to professional monitoring centers, AI-based technology can ensure that important events are never missed. This takes some of the burdens from the staff at the command center from having to be constantly vigilant. The benefit of AI-based technologies is that they never sleep, virtually eliminate false alarms, and deliver the true power of video analytics to provide real-time insight and intelligence.
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