Complete Perimeter Solution

Radar and deep learning technologies finally come together

• By Yaron Zussman
• Oct 01, 2018

An alarm is triggered in the middle of the night at a remote power plant. An offsite security patrol officer receives the alert via the video management system and springs into action, driving more than 30 minutes to the remote location in hopes of mitigating the threat before the situation escalates. Upon arrival, the security guard realizes the alert was simply an animal at the fence line that triggered a nuisance alarm. This scenario plays out all too often, whether it be at a utility substation or commercial business. False alarms are detrimental to security efforts, wasting both time and money.

This is just one of many instances where artificial intelligence (AI) and deep learning have become critical for security solutions to function optimally. While practically everyone in the security industry has been talking about AI and its possibilities, some are still unclear on how this technology works and what it has to offer.

A key trend for 2018 has been the integration of AI with various security systems. AI-enabled surveillance cameras, facial recognition software, and fingerprint authentication scanners are just a few examples. One unique AI technology pairing gaining traction in the perimeter security sector is radar. Thanks to its superior reliability and wide field of coverage, radar has emerged as a prime candidate for integration. Taking the most dependable radar sensors and optical sensors and merging these with deep learning programs via advanced video management software, enables higher-performing analytics and enhanced perimeter security. Before discussing this dynamic technology combination, let’s look at radar and its uses for the security industry.

Radar Technology

Radar stands for “Radio Detection and Ranging,” and uses radio frequency waves to identify and track targets. In simple terms, radar is an object detection system that sends out radio waves to determine the range, direction, speed, and altitude of an object. Unlike video or thermal imaging cameras, radar performance is not adversely affected by weather, lighting, or other external forces. Radar provides continuous coverage of any protected site in all weather and lighting conditions, allowing for volumetric perimeter protection while keeping power consumption to a minimum.

Radar has been commonly used in government and military applications, giving the misconception that it is only meant for highlevel deployments. However, radar’s wide coverage capabilities make it an ideal system for a variety of perimeter applications including, data centers, oil and gas refineries, logistic centers and education campuses.

With the ability to reach up to 120-degrees in azimuth and 30-degrees in elevation, a single radar is able to optimally cover an area despite changing topography. When radar is placed around the perimeter of a site, targets that approach the “fence line” will be automatically detected and tracked. For security forces using traditional video cameras alone, detecting humans or vehicles at far ranges can be near impossible, allowing threats to come dangerously close to protected assets. On the other hand, radar can detect humans or vehicles up to 1,000 meters away, alerting security forces long before they reach the boundary.

Integrating this technology with PTZ cameras enables slew-to-cue capabilities, allowing security personnel to have eyes on exactly what the radar is tracking for maximum situational awareness. New technology advancements have made radar even more effective. Here’s an overview behind the AI technology driving this enhancement.

AI and Deep Learning

The lines between artificial intelligence and deep learning can be unclear for many consumers. The easiest way to think of these technologies is in concentric circles, with AI being the largest or the umbrella concept, and deep learning in the center as it is the technology that enables AI’s abilities. Artificial intelligence refers to systems that perform tasks that identify patterns objects. In security, AI capabilities translate to greater data analysis and business intelligence.

For example, AI security systems can now detect and even recognize faces stored in a database, adding a whole new layer to duel-authentication access control and management. License plate recognition is another common AI function.

Deep learning is the recent breakthrough within the field of AI that allows for machines to classify objects and make decisions based upon what they have “learned.” Deep learning algorithms that are already being deployed can not only enable AI devices to learn and perceive their environments but can also learn to differentiate everyday occurrences from abnormalities. This pattern recognition may seem simple, but it is a huge victory for analytics software. By giving machines “brains” to match their “eyes,” these offerings allow for higher levels of accuracy and reliability in both object and behavior classification.

The Merging of Radar and Deep Learning

While still a relatively new integration, the concept of enhancing perimeter systems, featuring radar and other video technologies, with deep learning is taking hold in the security industry. The idea is simple, combine the best technology for target detection with the best technology for target classification and merge them to create a fused engine that yields the lowest possible number of false alarms. Some AI software work only off of a radar signal, but more advanced solutions are extending the AI to the accompanying video stream to analyze data from both sources to get the most accurate results.

In the latter scenario, it all begins with radar detection. Once the radar filters out false alerts, it sends the validated target tracks detects to the VMS, which in turn, cues an integrated PTZ camera to follow the movements of said target. As the PTZ camera follows the intruder, deep learning software analyzes the video stream and tracked movement from the radar to classify the object. Once the target has been classified and validated, which takes less than a single second, a verified alert is generated, and the system will log and record the event.

To better illustrate the point, take a large tech company with data centers in various geographic areas. One of the greatest challenges faced by these facilities is the fact that many are in remote areas with wildlife surrounding the perimeter. While the complexes must be protected from vandalism and physical intrusion, these sites are regularly plagued by nuisance alarms. The largest companies own tens of data centers. Even if the number of daily false alarms is below five, the company could receive dozens a day from all the data centers combined. This inundates security systems.

Many times, security guards or law enforcement will end up responding to erroneous alarms, which take their attention away from true threats. By utilizing a deep learning target classifier that analyzes data from both the radar and video stream, the nuisance alarm rate (NAR) can be significantly reduced. In fact, for successful deployments, the system can replace physical guards, heavily reducing security operating costs.

There is a clear demand for perimeter security solutions that are effective in all conditions, and that reduce the NAR. While the integration of radar, video and deep learning technologies are still in its infancy, development has been rapid and there is no doubt that this will continue. This new integrated solution enables security to run at maximum efficiency while keeping costs minimal, simply by marrying the most reliable sensors with the most advanced analytics software available.

This article originally appeared in the October 2018 issue of Security Today.