ATPL

AI · See and predict

Cameras that notice what matters.

For factories, shops, societies and warehouses with cameras nobody watches: vision models count, check and flag what a person would miss, and send the clip, not the whole day.

Describe the problemAll AI capabilities

Cameras unused

Cameras watching nothing useful

Cameras and images turned into counts, checks and alerts with the clip.

  • 01 · Eyes

    You have sixteen cameras recording and nobody looks at the footage until something has already gone wrong.

  • 02 · Miss

    Counting stock, people or vehicles by hand is slow, and the count is wrong by the time it is finished.

  • 03 · Clip

    Quality checks on the line depend on whoever is on shift that day.

One question per feed

Detection you can act on

  • 01

    Camera and lighting assessment

    We check each camera’s angle, resolution and lighting against the task before promising anything; many vision projects fail at the lens, not the model.

  • 02

    Detection for one question

    Presence, count, entry and exit, missing safety gear, a gate left open, a product out of spec: one defined question per camera, with a threshold you agree.

  • 03

    Alerts with the clip

    When something is flagged, the person on duty gets the short clip on WhatsApp or the dashboard, not a notification to go and search the recorder.

  • 04

    Edge or cloud processing

    Processing runs on a small box at the site where bandwidth is poor, or in the cloud where it is not; footage does not leave the premises unless you choose.

  • 05

    Labelled evaluation set

    A few hundred frames from your own cameras, labelled with your team, used to measure accuracy before and after every change.

One frame in

A frame becomes an event

What the camera saw, the rule that fired and the clip a person can review — signal, not a live stream to stare at.

What a client typed

We have a textile unit with cameras on the loom floor and at the gate, and I want to know when a loom has stopped for more than a few minutes and whether anyone is on the floor without safety shoes.

Brief ready

What it becomes

Problem
Loom stoppages and missing safety shoes go unnoticed because nobody watches the cameras.
Scale
One unit; cameras on the loom floor and at the gate; two shifts.
Systems
Existing IP cameras and a network video recorder; WhatsApp for the shift supervisor.
First step
Stoppage detection on two looms for two weeks, measured against the supervisor’s own log, before safety-gear detection is added.

How we work

AI Camera Security is ATPL’s own vision product, built on this approach; ATPL will confirm what may be said about its status and where it runs.

How it ships

Light, label, alert

  1. cam/01

    Understand

    We visit the site, pull a week of footage, and find out whether the question can actually be answered from what the cameras see.

  2. cam/02

    Design

    We fix the one question per camera, the alert path and the acceptable false-alarm rate on paper; the model is chosen last.

  3. cam/03

    Build

    The system runs for two weeks flagging silently while your team labels every alert as right or wrong; it goes live when the numbers hold.

  4. cam/04

    Stay

    Accuracy drifts as lighting, seasons and layouts change, so flagged frames are reviewed monthly and the model is refreshed when they slip.

Vision or CCTV

Where it fits, where it doesn’t

Saying when this is the wrong tool is the fastest way to be believed about when it is the right one.

Fits when

  • A question a person could answer by looking at the frame: is it there, how many, did it move, is it the wrong colour.
  • Sites with cameras already installed, or willing to place a camera where the question is.
  • Jobs where a short delay and an occasional false alarm are acceptable.

Doesn’t fit when

  • Questions the camera cannot see: a fault inside a machine, a defect smaller than a few pixels, a face in the dark.
  • Anything where a missed event is a safety or legal failure with no human backup.
  • Identifying individual people without a clear legal basis and consent; we decline this work.

Your data

Footage stays on your recorder or an edge box at your site; only flagged clips and counts leave it, to a dashboard you own. No footage is sent to a model provider for training, and vision models run on infrastructure you control. Retention periods for clips are set by you.

Chosen for the job

Python · Node.js · React

Decide the shape

Before you start

Plain answers on scope, data and what a first proof usually takes.

Computer vision turns existing cameras into counters and inspectors: it detects machine stoppages, counts items and vehicles, checks for safety gear, flags an open gate or a blocked aisle, and sends the clip to a person. ATPL scopes one question per camera and measures accuracy on your own footage before going live.

Often, yes. ATPL first checks each camera’s angle, resolution and lighting against the specific question; a camera placed for security recording may need repositioning or a second camera for counting or inspection. The assessment comes before any model work.

It depends on the camera, the lighting and the question, which is why ATPL quotes accuracy only after a two-week silent run on your site, with every alert labelled right or wrong by your team. Counting and presence detection are usually reliable; fine defect inspection needs controlled lighting.

No. It makes the guard’s hours count by saying when and where to look, instead of hoping they watch sixteen screens. ATPL’s AI Camera Security product is built on this idea: footage that tells you when to look, with a person still deciding what to do.

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