Help Security Teams Identify Events That Need Attention.
A mall or office tower can have hundreds of cameras and two people watching them. CamBrain AI picks out the handful of moments that need a person — an atrium filling past its limit, a fire lane blocked by a delivery, someone in the plant room after closing — and sends each one to whoever is responsible.
Simulated illustration, not real footage: what each detection checks and the alert it raises.
The problem
What you are up against.
Too many cameras, too few eyes
The control room sees a wall of tiles. What it needs is the one tile that matters, with the reason.
Limits are set by licence, not by eye
Occupancy limits, fire lanes and restricted rooms come from the fire NOC and the building rules. A count against the limit is better than a guess.
Shoppers and staff are not profiled
No age, gender, emotion or demographic profiling, and no tracking of individual shoppers.
What CamBrain AI can alert on
Use cases, with their readiness.
1 validated · 7 need site setup. Labels come from the product's capability register.
What the labels mean.
Simulation Watch larger
Crowd buildup
Validated
“The atrium fills during a weekend event.”
What CamBrain AI detects
A camera's count stays well above its usual level for several minutes.
Who gets the alert
The control room and the floor manager.
What happens next
Staff open more routes or manage entry, using the live view.
Measured live at Gaya from timestamps: about one to two minutes from a camera's reading to an officer's phone, with each camera read every 90–106 s. Count-based, against each camera's own usual level.
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Occupancy over the limit
Site setup required
“The food court passes its licensed capacity at lunchtime.”
What CamBrain AI detects
The count inside a zone passes the capacity you set for it.
Who gets the alert
The duty manager.
What happens next
Entry is managed until the count falls back.
Runs in production today; not yet measured on a held-out set, so it is measured on your cameras first Needs a zone drawn on your camera.
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Queue monitoring
Site setup required
“The billing queue at the hypermarket stretches past the escalator.”
What CamBrain AI detects
How many people are in the queue area, and the expected wait.
Who gets the alert
The store or operations manager.
What happens next
More counters are opened.
Built. Needs the queue area drawn and checked on your cameras.
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Blocked fire lane
Site setup required
“A delivery van parks in the fire lane outside the service entrance.”
What CamBrain AI detects
A vehicle or object stays in the fire-lane zone longer than the time you set.
Who gets the alert
Security at that entrance.
What happens next
The lane is cleared; the camera confirms before the alert closes.
Needs a zone drawn on your camera, then testing on your footage.
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Restricted-area entry
Site setup required
“Someone walks into the plant room on the service floor.”
What CamBrain AI detects
A person enters a zone marked restricted.
Who gets the alert
The facility manager and the control room.
What happens next
Security checks the clip and responds.
Runs in production today; not yet measured on a held-out set, so it is measured on your cameras first Needs a zone drawn on your camera.
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After-hours presence
Site setup required
“A shop's back corridor has movement an hour after closing.”
What CamBrain AI detects
A person is present in a zone during hours it should be empty.
Who gets the alert
Night security.
What happens next
A guard is sent to check.
Needs a zone drawn on your camera, the hours it applies, then testing on your footage.
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Tailgating at office badge doors
Site setup required
“Three people walk through on one badge swipe.”
What CamBrain AI detects
More people pass a controlled door than badges were presented.
Who gets the alert
The office security desk.
What happens next
Security reviews the clip.
Needs door events from your access-control system.
Built. Needs badge events from your access-control system.
Simulation Watch larger
Camera obstruction or tampering
Site setup required
“A camera in the parking basement is covered with a bag.”
What CamBrain AI detects
The view no longer matches its reference: covered, defocused, dark or moved.
Who gets the alert
The control room.
What happens next
Someone restores the camera.
Runs in production today; not yet measured on a held-out set, so it is measured on your cameras first.
When an alert fires
Who is told, and what they see.
Who receives it
The security control room and the floor or facility manager for that zone — on the control-room screen and on phones.
What the alert contains
Camera, floor and zone
The time, and the count against the limit where relevant
A snapshot and short clip
Who acknowledged it and when it closed
What they can do
Open the live view or the clip, acknowledge it, and
close it when it is dealt with. Alerts that a fix has been made are checked by the camera before
they close. The decision is always a person's.
Technical conditions
What your cameras need.
Cameras that see the atrium, entrances, fire lanes and service areas. Occupancy counts work best from a high, angled view. Tailgating and door checks need your access-control events.
Tracking individual shoppers or employees across the building
Guaranteed theft prevention or 'suspicious behaviour' prediction
Questions
Frequently asked.
Will it stop theft?
No video system can promise that. CamBrain AI helps your team notice specific, configured events sooner and find footage faster.
Does it profile shoppers?
No. It counts people in zones. It does not estimate age or gender, read emotion or follow individual shoppers.
Does it work with our VMS?
CamBrain AI reads streams over RTSP or ONVIF, from cameras directly or through a compatible NVR or VMS. We confirm compatibility on your system before a pilot.
How are counts checked?
During the pilot, counts are compared with hand counts on your own cameras, and the error is reported to you.
See it on your own cameras.
Tell us about your site and the problems you want to solve. We will check your cameras and recommend one focused use case to demonstrate first.