Camera vendors slap 'AI' on every product these days. Most of it is marketing. Here's what actually matters when AI is used in real-world security monitoring.
What AI is actually doing
In security video, AI is mostly used for three things:
1. Classification — is this a person, a car, an animal? 2. Behavior — is this person loitering, climbing, crossing a line? 3. Filtering — is this a real event or noise (weather, shadows, branches, wildlife)?
The value isn't 'detection' — that's been around for years. The value is *quality of filtering*, because the bottleneck in any monitoring system is operator attention. A model that catches everything but cries wolf 10x a night is worse than one that's slightly less sensitive but quiet.
Edge vs. cloud
Most on-camera AI is edge inference: cheap, fast, low-latency, but limited in accuracy. Cloud AI (running on GPUs) is more accurate but adds bandwidth and cost. The right answer is hybrid: use edge to filter the obvious noise, escalate to cloud for higher-fidelity classification.
Per-camera tuning matters more than the model
We've seen great models perform terribly because nobody tuned the camera. A 5-megapixel camera pointed into the sun, with default sensitivity, fires constantly on a tree branch. The same camera, masked correctly with zone rules, fires only on the events that matter.
Good security monitoring isn't 'install AI, walk away.' It's 'install AI, tune for the site, retune as the site changes.'
What to ask any AI vendor
- How do you tune for individual cameras? (Generic models alone are not enough.)
- What's your false-alert rate on real customer sites?
- Where does inference run? (Hybrid is best.)
- How do operators handle ambiguous AI events? (Humans should be in the loop.)
- How often do you retrain or retune?
How we use AI at VuePointSecure
At VuePointSecure, AI is a *filter* — it surfaces candidate events, and operators verify. We tune per camera, blend edge and cloud inference, and continuously feed misses and false alerts back into the system. The goal is simple: operators should only see events that matter.