Privacy.
How on-site processing makes video privacy the default, not a promise.
The government wrote down the risk itself
A federal privacy assessment for a system that fuses video, facial biometrics and plate data contains a sentence most vendors would never publish. It also describes exactly where the risk in computer vision has moved.
The problem was never the camera
Cities around Houston are exiting their license plate reader contracts. We build license plate recognition, so this one deserves a straight answer about architecture rather than a defence of anyone's product.
The camera backlash is a trust problem, not a camera problem
On the Fourth of July, residents in Lubbock protested to have their city's license-plate cameras removed. The lesson for everyone building safety tech is not about the hardware. It is about what you collect, where it goes, and who controls it.
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.
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.
Don't let the guide be the end of it. Take the checklist, a clear deployment path, and a direct line to ask implementation questions.
The checklist is built for operators evaluating a live pilot in the next 30 days.
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See the flow on a real operating scenario and scope a pilot around one facility or corridor.
See deployment architecture
Review camera ingest, edge inference, alert routing, and what stays on-premises.
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Bring camera count, VMS constraints, latency expectations, and privacy requirements to a technical review.