Shooter detection part 1: Addressing false alarms

Shooter Detection series - reducing false alarms

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In the first article of a new limited series, Stephen Carney, President, Shooter Detection Systems (SDS) explores how to enhance confidence in active shooter detection technology.

Shooter detection solutions

The security landscape is constantly evolving with advancements in technology-driven solutions to mitigate risks and respond to incidents.

Gunshot detection systems and video analytics for weapons detection are two notable advancements gaining traction in shooter detection.

This article aims to explore the differences between these approaches to help organizations choose the most suitable solution for their security needs.

Gunshot detection systems

Gunshot detection systems can employ a range of technologies, such as acoustic sensors, machine learning, infrared detection and detection algorithms to identify gunfire.

Upon detecting a gunshot, these systems promptly notify law enforcement and security personnel, enabling a swift response to potential threats.

There are, however, major differences in technology development approaches among vendors, so it is important to understand the key capabilities needed for an effective system.

Accurate algorithms capable of filtering out background noise are vital to ensure precise gunshot detection and minimize false alarms.

Systems that detect the infrared energy discharged by the gunpowder combustion, which does not rely on line of sight to the weapon, offer greater accuracy and a dramatic reduction in false alarms.

False alarm avoidance is an increasing focus in the industry as the risks associated with them become more evident.

A recent news story reported a frisbee had struck an acoustic-only type of gunshot sensor in a school, triggering the gunshot detection system to report gunfire, leading to a police active shooter response.

Children were unnecessarily frightened and parents expressed a loss in confidence in the technology’s ability to accurately detect gunshots.

In another school local to SDS, police received a call stating an active shooter was attacking a school, when in fact it was a hoax, or “swatting” call, delivered specifically to draw a law enforcement SWAT response.

During police response, an accidental weapon discharge occurred in the facility as a result.

This latter situation seriously underscores our need to be vigilant against false alarms. Whether a false alert to a non-gunshot incident or a swatting call, the risk is the same.

Every false response triggered will result in a loss in technology confidence, potentially slower future reaction times and raises the potential for unnecessary gunfire during response efforts.

This is why we are seeing buyers increasingly benchmarking gunshot detection systems for a host of potential false alarm conditions to ensure confidence in the system.

While a key value to gunshot detection systems is the ability to reduce response times, what is more important is increasing the accuracy of the first responders’ situational awareness.

Systems requiring external validation, such as monitoring centers or audio analysis, introduce delays that reduce the system’s effectiveness and involve an additional layer of human error to misinterpret the situation.

Systems that can quickly decipher the presence of gunfire and deliver information about the location, sequence and direction of movement of the shooter just as fast will significantly improve the overall emergency awareness of an unfolding situation.

Video analytics

Weapons detection, also known as video analytics or gun detection, is an emerging field that leverages the power of AI and machine vision to identify weapons through surveillance cameras.

By employing advanced algorithms, this technology aims to enhance security measures by swiftly detecting potential threats in real-time.

In practice, the system captures images or video footage for shooter detection through surveillance cameras and analyzes them using AI algorithms.

The AI algorithms analyze visual data, seeking specific patterns and characteristics associated with weapons.

Once a potential weapon is identified, the system alerts an operator, who then validates the image and determines the likelihood of the object being a weapon or something harmless.

This verification process ensures that only legitimate threats get reported to the security end user.

While the concept of weapons detection appears promising, it is not without its limitations.

False positives can occur due to the inherent challenges in object recognition. Factors such as lighting conditions, image quality and occlusions can lead to misidentifications, where harmless objects may be mistakenly flagged as weapons.

This underscores the need for human validation to ensure accurate threat assessment for shooter detection.

From a risk perspective, it is important to consider that these systems rely on capturing a visual image of the weapon, which implies that the perpetrator has already revealed the weapon and intends to use it.

This raises concerns about the effectiveness of video analytics as a proactive measure to mitigate active shooter risks.

In contrast, gunshot detection technology offers a distinct advantage in terms of accuracy, speed and responsiveness.

Gunshot detection allows for immediate notification and response, alerting law enforcement authorities to the precise location of the gunshot and enabling them to intervene rapidly.

In situations involving an active incident, the ability to swiftly alert law enforcement after shooter detection is of paramount importance.

Gunshot detection technology provides a critical advantage in this regard, as it enables a faster response time compared to video analytics.

By immediately notifying authorities without relying on visual confirmation, gunshot detection systems can help mitigate the threat posed by an active shooter and potentially save lives.

When determining which solution best fits a given organization’s security needs for shooter detection, it is essential to consider the nature of risks, facility layout and operational requirements.

Ideally, combining both technologies may create a comprehensive security solution that enhances the effectiveness of shooter detection and prevention, leading to a safer environment for all.

Make sure to keep an eye out for the next installment of the Shooter detection miniseries, coming 29 August.

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