Active shooter incidents are among the most terrifying acts of violence facing Americans today. Whether they happen in a subway station, a place of worship, or place of business, they seemingly come out of nowhere and devastate lives in a matter of moments.
The trauma and mental health crisis that has emerged from the pandemic has only exacerbated the issue, leading to an unprecedented increase in active shooter incidents. In the past two years from 2020 to 2022, the United States has recorded an increase in mass shootings. According to the Department of Justice (DOJ), these two years represent the highest rates of mass shootings in history.
For security teams, active shooter incidents present numerous challenges. In the middle of the confusion that comes with active shooter incidents, they must detect the incident as quickly as possible, report to responders, and provide continuous updates as the situation evolves. In this blog, we’ll examine an active shooter incident and look at where computer vision intelligence can aid teams in detecting and responding.
Active Shooter at the Washington Navy Yard
On April 16, 2012, Aaron Alexis entered the Washington Navy Yard just before 8 a.m. Although he had been put on leave months earlier due to concerning behavior and bouts of paranoia, Alexis entered the premises using a valid access pass.
Minutes later, he entered building 197 with a disassembled shotgun in a shoulder bag. He went to the fourth floor bathroom, assembled the shotgun, crossed the hallway and began firing. A call was put into 911 minutes later, and officers from the D.C. Metropolitan Police Department and several other law enforcement agencies arrived within minutes at 8:32 a.m.
Despite the fast response, the shooter would remain in the building for more than an hour, changing location, searching for targets, and finally concealing himself on the third floor. By the time that the incident concluded, 13 people in total were killed, including the shooter.
As is often the case following gunfire, the ability of law enforcement to respond was hampered by confusion.
Responders struggled to quickly get accurate reporting on the situation and had trouble locating the exact building. There was confusion about which building the shooting had taken place due to a wounded victim being moved to an adjacent building for medical attention. They would eventually head in the direction that people were fleeing.
At one point, authorities thought that there might be a second shooter. And near the end of the confrontation, the shooter was able to jump out from his hiding spot on the 3rd floor and fire upon an officer from five feet away, hitting him twice in his tactical vest.
Real-time context with computer vision intelligence
While technology cannot always prevent these types of active shooter incidents, it can significantly improve security team response and potentially save lives in the process. Let’s take a look at the key points in an active shooter incident to see how an existing security camera system, enabled by computer vision intelligence could aid security team response to similar incidents.
Early indicators: In the Washington Navy Yard shooting, the shooter was a civilian contractor with valid credentials, which enabled authorized entry despite the access control system that was in place.
In other cases, however, like the YouTube headquarters shooting that took place in 2018, an unauthorized person evades the access control system in some way. Detecting when someone evades access control systems is difficult. The indicators can be as subtle as someone loitering outside of an entrance.
Because early visual indicators of threats like loitering and tailgating bypass physical badge scans and fob reads, physical access control systems (PACS) are unable to detect them. Contrast this with computer vision intelligence, which leverages existing security cameras to detect these indicators visually and with a high degree of accuracy by using computer vision algorithms in real-time to analyze surveillance video feeds. These early indicators can provide security teams with the time they need to intervene before a situation escalates.
By adding visual confirmation, computer vision intelligence complements PACS. In many cases, computer vision intelligence adds valuable context to the alerts generated by physical access systems (PACS).
Detection: In the Navy Yard shooting, a 911 call was made shortly after the shooting started. Law enforcement arrived soon thereafter but had trouble locating the building and, at one point, thought that there may have been a second shooter – adding confusion to their response.
Computer vision intelligence automatically detects threat signatures in real-time, including detecting when a person is brandishing a firearm. Computer vision intelligence automatically and continuously monitors camera surveillance video, scanning scenes for threat signatures of concern. Once a person is identified brandishing a firearm, an alert is automatically sent to the security team and responders.
The alert includes the surveillance clip that triggered the threat signature identification. Everyone who needs to be notified, including law enforcement, the head of security, the security operations center, and more, have immediate access to visual confirmation of the threat. With this extra context and early notification, responders can visually identify the shooter, the weapon, and location, reducing confusion as an incident unfolds. In the case that additional shooters are present, subsequent alerts would be sent, each visually identifying the additional shooters, the weapon, and the location of the incident.
Incident response: In the Navy Yard shooting, more than an hour elapsed from the time law enforcement arrived to the time the shooter was immobilized. In that time, the shooter moved from area to area and eventually hid himself on the third floor, where he was able to fire upon an officer from close range.
During an active shooter incident, computer vision intelligence products provide continual updates to responders by tracking the perpetrators behavior as they move from area to area. Security teams can share this context directly with responders, enabling them to relay key information as the situation evolves, including where the shooter is located and whether the shooter still has a weapon or has changed weapon.
In addition to clarifying the behavior of perpetrators as an incident evolves, computer vision intelligence products include the capability to identify and clarify other security and safety issues as an incident evolves. For instance, alerting the response team to where there are sudden exits or when a person falls and may need medical attention. Computer Vision Intelligence immediately provides a central point of visual verification for incident tracking and response, providing context in a chaotic situation.
Post incident: While the focus is on preventing and responding to the active shooter incident, computer vision intelligence also provides valuable information that can be used to improve security in the future, identifying weaknesses in the perimeter or policies that can help prevent future incidents.
Leveraging forensics applied to the video surveillance feeds provided by computer vision intelligence products, security teams can retrace an incident identifying how an individual entered the premises starting with any early indicator, like whether tailgating occurred. From there, the security team can view the individual’s subsequent behaviors – providing a full picture of the incident.
Preparing for an active shooter incident
The Navy Yard shooting was a terrible tragedy, and it has sadly become one of many active shooter tragedies that have occurred in the United States over the last several years. Gun violence has become a critical security scenario that every organization needs to consider and prepare for. Technologies like computer vision intelligence can be a valuable resource at every step of an active shooter incident.
Our mission here at Ambient.ai has always been to prevent every security incident possible. We’re focused on doing this by making computer vision intelligence technology accessible through applications designed to shift security from a reactive practice into a proactive practice. Active shooter incidents present a considerable challenge to security teams. Computer vision intelligence can help address some of the biggest challenges facing security teams, helping to detect early threat indicators, helping responders cut through the confusion, assisting with identifying and tracking the shooter, providing continuous real-time updates as the situation evolves, and facilitating the retrieval and sharing of important information once the incident is resolved.
To learn more about how computer vision intelligence works, download our whitepaper, Entering the Era of Computer Vision Intelligence. For more information about Ambient.ai, contact us, or request a demo.