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Arcade Alliance

Live demo · AI tool · Stores, malls, events & campuses

Footfall, conversion and queues — from cameras you already have.

Existing cameras count who walks in, where they go and how long the billing queue gets. Matched with your POS, you finally see conversion by hour — and when to put more staff on the floor.

Try it live Make it yours

The problem

“We know how many people buy — but not how many walk in.”
The problem this demo is built to solveLive demo · sample data

What is people counting AI?

People counting AI uses existing CCTV cameras to count how many people enter a store, mall or campus, hour by hour and zone by zone. Compared with bills from the POS, it gives conversion — the share of visitors who buy. It also measures time spent in each zone and billing queues, and stores only counts, not faces.

Built around your workflow: this demo is a starting point — screens, fields, rules and integrations are made to fit your business.

Store conversion

Before: Not measurableWith the system: By hour, every day

How the system works · how we measured

In the demo footfall counted from cameras is set against POS bills for each hour, which gives conversion; a built system checks its counts against manual samples.

01 · The problem

What problem does it solve?

A fashion retailer with three stores tracks sales and bills but has no idea how many people walk in. Evening sales look good, so the stores staff evenly through the day.

Shoppers leave long billing queues on weekends, and nobody can say whether a slow day was fewer visitors or fewer buyers.

  • Unknown conversion

    Sales without the visitors behind them.

  • Wrong staffing

    Too few people when the floor is busiest.

  • Walk-outs

    Shoppers who leave long queues.

  • Layout guesses

    No idea which zones people ignore.

02 · The solution

How does People Counting AI fix it?

  1. 01

    Count at the doors

    People crossing entrance lines, in and out, from existing cameras where the angle allows.

  2. 02

    Leave out staff

    By uniform and staff-only areas, so counts are shoppers only.

  3. 03

    Zones and queues

    Time spent in each zone, and billing-queue length with alerts.

  4. 04

    Join with sales

    Footfall against POS bills gives conversion by hour, day and store.

03 · The result

What changes?

Store conversion

Before: Not measurableWith the system: By hour, every day

How the system works · how we measured

In the demo footfall counted from cameras is set against POS bills for each hour, which gives conversion; a built system checks its counts against manual samples.

CCTV, usually
Existing
Footfall vs bills, by hour
Conversion
Alerts before they get long
Queues
Only counts are stored
No faces

Live demo

Try it live.

A scripted demo with sample data, so it behaves the same for everyone. Nothing you do here is saved or sent anywhere.
  1. 1Compare people in and bills by hour.
  2. 2Alert the manager of the store with low conversion.
  3. 3Count the last hour from CCTV.
Make it yours
Loading the demo…

Before & after

The same day, two ways.

Watch each task move from the way it’s done today to the way it runs on the system — or drag the slider yourself.
Today
With the system
  • VisitorsToday: UnknownWith the system: Counted by hour
  • ConversionToday: Not measurableWith the system: Footfall vs bills
  • QueuesToday: Seen when it’s too lateWith the system: Alert at 6 people
  • StaffingToday: Even through the dayWith the system: Matched to footfall

Your numbers

What could it save you?

Put in your own volume and timing. It’s your estimate, worked out in your browser — not a promise from us.
min
min

Our estimate for the same task — change it if you like.

₹ / hr

Your estimate

91 hours a month

About 11.4 working days back every month.

91 manual footfall counts a month × 60.0 minutes saved each. Your numbers stay in this browser.

Make it yours

Built around your business.

Choose what your version of People Counting AI needs. Your brief builds itself — send it, and we reply with a plan.

Often asked for by businesses like yours

  • Entrances and zones
  • How staff are excluded
  • Queue thresholds
  • Opening hours
  • Privacy notices at the door
Always included
  • Footfall counting
  • Conversion with POS
  • Queue alerts
  • Zone dwell time
Add modules
Connect to the tools you use
Runs on
Languages
Hosting
Team size

Under the hood

How is it built?

Where the data comes from, what the system does with it, and what comes out.

Inputs

  • Existing CCTV streams
  • Store zones & entrance lines
  • POS bills by hour

The engine

  • Person detection & line counting
  • Staff exclusion
  • Queue & dwell measurement

Outputs

  • Footfall & conversion dashboard
  • Queue alerts
  • Weekly store comparison

Modules

  • Footfall by hour
  • Zone occupancy
  • Dwell time
  • Queue alerts
  • Footfall-to-sales conversion
  • Privacy by design

Connects to

  • Existing CCTV / NVR
  • Your POS
  • WhatsApp Business API
  • BI dashboards

Stack

  • Person detection models (YOLO family)
  • Edge box in the store
  • RTSP from existing CCTV
  • POS data connector
  • PostgreSQL
  • WhatsApp alerts

Proof, the honest way

What would you measure?

This is a demo, so we don’t claim client results. In a real roll-out, these are the numbers we track from week one — before and after.

  • Count accuracy

    Checked against manual counts each month.

  • Conversion

    By store and hour.

  • Queue time

    At peak hours.

  • Staffing match

    Staff on the floor vs footfall.

FAQ

People Counting AI FAQ

All questions →
What is people counting AI?

People counting AI uses existing CCTV cameras to count how many people enter a store, mall or campus, hour by hour and zone by zone. Compared with bills from the POS, it gives conversion — the share of visitors who buy. It also measures time spent in each zone and billing queues, and stores only counts, not faces.

Does it work with our existing cameras?

Usually. Cameras above the entrance at a steep angle count best; we check each one and suggest a small change where a camera can’t see the line.

Does it recognise faces?

No. It detects people as shapes to count them; no faces are identified or stored, and only counts are kept.

How accurate are the counts?

It depends on the camera and the entrance. We measure it against manual counts during setup and keep checking it each month.

Can it tell staff from shoppers?

Yes, by uniform colour and staff-only areas, which keeps conversion figures honest.

Is it only for retail?

No — malls, events, campuses, canteens and offices use the same counting for occupancy and planning.

Built around you

Every demo is a starting point.

Nothing here is sold as-is. Each one is rebuilt around your business — these are the things we tailor on every project.

  • Your workflow

    Steps, approvals and statuses the way your team works today.

  • Roles & permissions

    Who sees and changes what — by branch, team or person.

  • Fields & forms

    Your terms, your fields, your numbering and units.

  • Reports

    The numbers you check every day, in your format.

  • Integrations

    Tally, WhatsApp, payments, couriers and the tools you already use.

  • Your brand

    Your name, logo and colours on every screen and document.

  • Language

    English, Hindi or a regional language for staff and customers.

  • Hosting

    Our cloud, your cloud account or your own server.

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Updated 30 Oct 2026 · Arcade Alliance Network is a web, app, software and AI development company in Lucknow, India, building custom software for businesses in India and worldwide.

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