Introduction to Artificial Intelligence in Video Security
For all the progress in artificial intelligence, most video security systems still fail at recognising context in real-world conditions. The majority of cameras can capture real-time footage, but struggle to interpret it. This is a problem turning into a growing concern for smart city designers, manufacturers and schools, each of which may depend on AI to keep people and property safe.
The Problem with Traditional Video Security Systems
Lumana, an AI video surveillance company, believes the fault in these systems lies deep in the foundations of how they are built. “Traditional video platforms were created decades ago to record footage, not interpret it,” said Jordan Shou, Lumana’s Vice President of Marketing. “Adding AI on top of outdated infrastructure is like putting a smart chip in a rotary phone. It might function, but it will never be truly intelligent or reliable enough to understand what’s being captured or help teams make smarter real-time decisions.”
Big Consequences
When traditional video security systems layer AI on older infrastructure, false alerts and performance issues arise. Alerts and missed detections are not just technical hiccups, but risks that can have devastating consequences. Shou points to a recent case where a school surveillance system, which used an AI add-on for gun detection, misidentified a harmless object for a weapon, setting off an unnecessary police response.
Errors and Costs
“Every mistake, whether it’s a missed event or a false alert, which leads to improper response, erodes trust,” he said. “It wastes time, money, and can traumatise people who did nothing wrong.” Errors can also be costly. Each false alarm forces teams to pause real work and investigate, a process that can drain millions from public safety and operational budgets every year.
Building a Smarter Foundation
Instead of layering AI on top of old video security frameworks, Lumana rebuilt the infrastructure itself with an all-in-one platform that combines modern video security hardware, software, and proprietary AI. The company’s hybrid-cloud design connects any security camera to GPU-powered processors and adaptive AI models that operate at the edge – meaning they are located as close as possible to where the footage is captured.
Improved Performance and Accuracy
The result, Shou says, is faster performance and more accurate analysis. Each camera becomes a continuous-learning device that improves over time, understanding motion, behaviour, and patterns unique to its environment. “The issue is that most of today’s video surveillance systems use static, off-the-shelf AI models that were only designed to work in specific environments. AI shouldn’t need a perfect lab environment to work,” Shou explained.
Real-World Use Cases
Lumana’s systems have been deployed in several industries. One of its most visible projects is with JKK Pack, a 24-hour packaging manufacturer that uses security cameras to monitor safety and operational efficiency in its facilities. Before Lumana’s deployment, cameras only recorded incidents for later review, which led to missed events and reactive incident response. After the upgrade, the same hardware could detect unsafe movements, equipment faults, or manufacturing bottlenecks in real-time.
Improved Response Time
The company reported 90% faster investigations and alerts delivered in under a second which dramatically improved response to safety incidents, without replacing a single camera. In another deployment, a grocery retailer integrated Lumana’s AI into its existing camera network to flag unusual point-of-sale activity, like repeat voids, and to correlate those events with visual evidence.
A Broader Push for Reliable AI Video Security
Lumana’s work comes at a time when accuracy and accountability are replacing speed as the top priorities for enterprise AI. A recent study from F5 found that only 2% of companies consider themselves fully ready to scale AI, with governance and data security cited as the main challenges. That caution is reflected in the market, with analysts warning that as AI takes on more decision-making, systems must remain “auditable, transparent, and free from bias.”
The Next Step in Machine Vision
Shou said Lumana’s next stage of development aims to move from detection and understanding to predicting. “The next evolution of AI video will be about reasoning,” he said. “The ability to grasp context in real time, provide actionable and impactful insights from the video data collected, will change how we think about safety, operations, and awareness.”
Conclusion
In conclusion, traditional video security systems are struggling to keep up with the demands of real-world conditions, and AI add-ons are not enough to solve the problem. Lumana’s approach to rebuilding the infrastructure with a focus on AI, performance, and control is a step in the right direction. As the company continues to develop its technology, we can expect to see more accurate and reliable video security systems that can help organizations make smarter decisions.
FAQs
Q: What is the main problem with traditional video security systems?
A: The main problem is that they were created decades ago to record footage, not interpret it, and adding AI on top of outdated infrastructure is not enough to solve the problem.
Q: What is Lumana’s approach to video security?
A: Lumana rebuilt the infrastructure itself with an all-in-one platform that combines modern video security hardware, software, and proprietary AI.
Q: What are the benefits of Lumana’s system?
A: The benefits include faster performance, more accurate analysis, and the ability to detect unsafe movements, equipment faults, or manufacturing bottlenecks in real-time.
Q: What is the next step in machine vision?
A: The next step is to move from detection and understanding to predicting, with the ability to grasp context in real time and provide actionable insights from the video data collected.
