Archive
How computer vision actually works for physical security.
The case for AI smoke detectors is written in three numbers: fire deaths are rising, the survivable window has collapsed, and the property loss concentrates in…
Detecting a phone in a classroom is not a metal-detector problem. It is a computer-vision problem about hands, posture, and a small dark rectangle the camera…
Following one person across a building's cameras is a measured computer-vision discipline that needs no facial recognition. A technical reference on multi-object tracking, person re-identification, the…
Three numbers explain why AI video analytics succeed on some camera estates and quietly underperform on others: the pixel density the international standard demands, the accuracy…
Retrofit, not rip-and-replace, is the dominant deployment pattern for AI physical security in 2026. The standards layer is the reason it works.
Where the AI model runs has quietly become the most consequential procurement question in physical security. 10–20×Latency advantage of edge inference over cloud-routed inference for the…
Gun detection has moved from research lab to live deployment in under five years. Today, machine learning models run on camera feeds at schools, hospitals, stadiums,…
The architectural shift in physical security has nothing to do with cameras. It has to do with where the intelligence sits and when it acts. 98%of…
The economic and clinical case for AI fall detection now sits on three numbers that make the technology impossible to leave out of a 2026 safety…
A technical briefing on the three failure modes that determine real-world detection accuracy in physical security computer vision: occlusion, low-light operation, and adversarial conditions. What the…
