Video Surveillance

The new rules of security: How AI will transform video surveillance

The security industry is at a tipping point. For decades, we’ve relied on a trusted playbook that’s guided how we deploy resources, design solutions and adopt technology. But AI and machine learning are quickly transforming the security landscape. And that means it’s time for our legacy playbook to get a rewrite.

When it comes to intelligent video surveillance in particular, AI-driven products are beginning to unlock new functionality, and even change the role video surveillance plays for companies. From better sensors to higher resolution cameras to more efficient processing units, we’re seeing an unparalleled convergence of hardware and software. And that’s creating new opportunities for everything from intelligent threat detection to personalized customer experiences.

We’re just at the beginning of this journey, but it’s clear that best practices are changing. Seemingly in real-time, security professionals are reimagining how they’ll build their teams, structure engagements and define their value. We’re all still building the playbook as we use it, but here are four new, unspoken “rules” for the new world of security – and how they’ll continue to evolve thanks to AI.

 

1. Embrace flexible setups

Before recent technological advancements, most security systems only featured motion sensors that triggered alerts based on a percentage of pixels that changed on a screen. That meant security teams spent a lot of time tweaking the camera setup to minimize false positives. In a highly manual and time-intensive process, you could avoid falling leaves or passing headlights that might set off alerts. But it came at the cost of restricting the field of view and leaving certain areas vulnerable to threats.

With AI, you don’t need to compromise. Smart systems can identify the difference between humans, animals, and objects and then trigger alerts based only on those identified signatures regardless of surrounding video noise like blowing leaves, headlights, inclement weather or other factors. This capability allows you to focus specifically on your security goals, which means you can expand your field of view as wide as necessary.

And with new flexibility comes new opportunities for adaptation and evolution. In the new era, security will need to anticipate new threats and risks. Leading-edge professionals will harness the new setup flexibility to experiment with new approaches and proactively optimize.

 

2. Maximize economies of scale

With so many false positives, legacy security systems didn’t exactly inspire confidence when it came to the accuracy of alerts. What’s worse, stray foliage or flashes of light could generate hundreds of alerts per night – far too many to responsively address. AI-powered platforms significantly reduce the number of false positives, allowing security teams to take a more cost-effective approach to staffing.

With intelligent video surveillance, companies can deploy a lean team of remote monitors to keep tabs on several locations. Additionally, they can use smart platforms to communicate with any trespassers or troublemakers in real time and dispatch law enforcement or emergency responders if necessary.

 

3. Look for behavior patterns, not faces

The General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) have created a great deal of anxiety in the industry about privacy. In the world of video, AI-related privacy conversations have largely focused on identity revealing technologies like facial recognition. Critics argue that identity aside, AI could develop algorithms that introduce unfair race or gender biases.

But we think the security benefits from smart video surveillance systems come from identifying specific behavior patterns, not faces. By analyzing common behavior patterns in retail environments, artificial intelligence and machine learning systems can learn to spot abnormal behavior — or suspicious activity that could indicate a problem without ever identifying the individual or inferring any unnecessary bias.

For example, most consumers tend to select one high-value item from a given shelf or approach the register in a moderate gait. So when surveillance platforms detect behavior that deviates from these norms, they can alert security teams to potential threats and risks. Additionally, by shifting from a reactive model that targets individuals to a proactive model that anticipates behaviors, we can avoid scenarios that profile individuals or invite bias.

 

4. Focus on relationship-building, not bad-guy busting

Prioritizing behavior over faces means surveillance systems can go far beyond pinpointing the bad guy. It means they can actually help build and strengthen relationships with valuable customers.

Especially at a time when retail is looking for new ways to engage customers, companies can use AI to identify signs of someone in need. For example, if the security platform flags a person making sudden body movements or wandering back and forth, it could mean a customer is frustrated or confused. Sending over an employee to help could not only close a sale but also boost customer loyalty.

By expanding our goals beyond threat detection, prevention and mitigation to the enhancement of customer satisfaction and brand value, we can create opportunities to improve the customer experiences and build relationships.

As we continue to train our models, we’re learning more and more about how AI can benefit – and redefine — security. And that means these new “rules” are hardly written in stone. If anything, the final and most important rule might be this: In a world where AI and machine learning push us to new levels of agility and adaptability, the playbook itself is always a work in progress.

Related Articles

Leave a Reply

Your email address will not be published.

Back to top button