The DHI Blog.
Perspectives on edge AI, video privacy, and deploying safety analytics on the cameras you already have. No hype, just how this actually works.
The real divide in camera AI isn't capability. It's control.
A US city wants facial recognition on its buses, and the fight that broke out misses the point. The line between safety and surveillance was never the technology. It is who controls it, where it runs, and what it is pointed at.
Every warehouse has cameras. Almost none have early warning.
A Los Angeles cold-storage warehouse burned for eight days. The hard lesson for safety teams isn't about fire codes: it's the gap between a camera that records an incident and one that catches it early enough to matter.
Who can turn your AI off?
In June 2026, the most powerful AI models on the market were gated, pulled, and partially reinstated by parties their customers don't control. For most software that's a policy story. For safety-critical systems, it's an ownership question.
Nobody answers the alarm anymore
When most camera alerts are shadows, rain, and headlights, your team learns to ignore all of them, including the one that mattered. Why alarm fatigue is a detection problem, not a discipline problem.
You don't need new cameras
Rip-and-replace is what kills safety projects. How to add AI to the CCTV and VMS you already run, starting with a single camera.
Your "edge AI" might just be the cloud with extra steps
A cloud provider just discontinued its edge-vision product. Here's what it reveals about how most camera AI is really built, and the one question that separates real edge from rented edge.
Where does your video actually go?
The question that quietly stalls AI camera projects, and why keeping footage on-site turns privacy from a liability into the default.
Use the guide, then validate it on your cameras.
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