Cybersecurity most often comes to mind when people think of AI in security. Things like automated vulnerability scanning and fraud detection have revolutionized the industry, but cyberspace is just the beginning of AI’s potential for safety.
It has many applications for physical security, too.
One of the most useful types of AI for the physical security industry is computer vision. Machine learning algorithms analyzing real-time video footage are starting to see widespread use across the sector, and it’s yielded some impressive results.
Here are eight of the most significant benefits of computer vision in security.
1. Biometric Authentication
The most familiar example of computer vision for security is biometric authentication. Roughly
That’s an impressive leap, but this tech can go far beyond simply unlocking a smartphone.
Many restricted-access areas today use keys, codes, or keycards to let authorized users in, but these solutions are susceptible to theft. Facial biometrics offer a safer alternative because criminals can’t steal someone’s face.
Because machine learning works so quickly, biometric locks are also faster than using a key or punching in a code. Government agencies or server rooms can use these systems to ensure that only authorized people enter without sacrificing efficiency.
2. Finding People of Interest
Many security agencies use computer vision today to detect people of interest with CCTV cameras. Law enforcement officials with a picture of a wanted criminal can use AI to scan a crowd and find them faster and more accurately than a manual search.
Officers can then detain them before they endanger anyone.
Using facial recognition to find criminals is controversial, as it raises questions about privacy and due process. However, that’s not the only way security forces can use computer vision to find people of interest.
Police in New Delhi used it to
3. Improved Weapon Detection
Facial recognition may be the most widely publicized application of computer vision in security, but it’s far from the only one. Machine vision tools can also detect weapons and other dangerous objects.
Deep learning algorithms can use real-life footage as well as pictures and scenes from movies to recognize weapons. They can then scan CCTV information in real-time to alert security teams when someone in a crowd may have a dangerous object.
Because AI is faster and more accurate than humans, this offers a more efficient and reliable way to secure areas than pat-downs or other manual inspections.
More than
This provides an easy, reliable way to separate legal gun carriers from criminal activity.
4. Faster Responses to Suspicious Situations
One of the biggest advantages of computer vision for security in any context is its speed. Many security teams don’t have the staffing levels to monitor CCTV footage 24/7, and even if they did, people could still miss things.
AI can detect suspicious situations faster and more accurately, leading to quicker, more effective responses.
In 2021,
Alternatively, cameras in crowded areas could detect fighting and send police to help mugging victims before the criminals escape.
5. Helping Understand Crowd Behavior
Computer vision is particularly helpful for crowded events like concerts or large sporting events. These algorithms can analyze footage from past scenarios to learn how people tend to react under different circumstances.
They can then reveal what changes can reduce confusion and improve safety in the future.
An AI model may reveal that crowds in a stadium often cram into a single exit, hindering a safe, orderly evacuation. Teams can then address the issue by making signs for other routes easier to see or widening paths to additional doors.
These ongoing changes can make emergency responses increasingly safe over time.
6. Virtual Fencing
Another benefit of computer vision in the security industry is the ability to set up virtual fencing. This technology uses machine vision to detect when people or objects cross into a specific area, even without a physical barrier.
That way, security teams can catch intruders in restricted areas without the staffing requirements and possibility of human error that come with manual monitoring.
Simpler technologies like laser tripwires can detect movement but can’t differentiate between different objects. Machine vision algorithms today
This nuance enables more effective responses to different situations and minimizes false positives.
7. Vehicle Identification
Machine vision systems can also help security forces identify vehicles. These algorithms can scan CCTV and dashcam footage for license plate numbers and other identifiers to aid law enforcement agencies or insurance claims.
Hit-and-run car accidents
Other computer vision systems throughout the city can then look for the car in question, helping catch the party responsible.
Similarly, police departments can use computer vision to scan traffic footage for stolen vehicles. They can then track these cars down far faster and more accurately, minimizing the potential for human error.
8. Streamlining Security Footage Reviews
Computer vision systems with security camera footage streamline the incident review process. Teams often review these video feeds after a break-in, traffic collision, or other incidents to learn more about what happened.
However, doing so manually could mean sifting through hours of unedited footage for minute details. AI offers a faster solution.
After reviewing enough video feeds, machine learning models will learn what normal activity looks like. They can then pinpoint when something unusual happens, letting them direct security teams to the exact moment where an incident occurred.
This, combined with computer vision’s ability to highlight suspicious people and objects, makes the review process far more efficient, leading to quicker responses.
Computer Vision Could Revolutionize Security
AI has transformed cybersecurity, and it could do the same for physical security. Some challenges remain, but the thoughtful implementation of computer vision could make the world safer. Teams can react faster, more accurately, and less obstructively with the help of this technology.