Staff Exclusion

Staff movements often make up a large portion of the activity inside a store. This feature filters staff out of your visitor counts so that the numbers reflect true customer traffic.

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“A built-in feature that automatically filters out staff movements from your visitor counts”

Using advanced vision intelligence, the system understands typical staff behaviour - such as standing behind the counter, helping customers, or restocking shelves, and automatically excludes these movements from the visitor count. There’s no need for extra hardware, no badges to wear, and no manual settings to update. It just works quietly in the background to keep your data accurate.

The system is built around four design principles:

Why past methods didn’t work

Before AI-based recognition, the industry tried many different techniques. Each had practical limitations:

Previous Method

Why It Didn’t Work in Real Life

Staff Button

Depends on staff remembering to press it; inconsistent and unreliable.

Staff Exclusion Line

Only works if staff always walk through a specific path; easy to bypass unintentionally.

Printed Badge / Tag on Chest

Requires consistent wearing; obstructed easily; visibility depends on camera angle.

Reflective Patches on Shoulders

Works only under ideal lighting and camera angles; not suitable for varied uniforms.

Bluetooth Beacons

Requires battery, pairing, and maintenance; signal often unstable; not universally accepted.

Our Approach

The system uses visual cues and movement patterns to recognise staff automatically.

Examples include:

  • Distinctive uniform colours or shapes (if available)
  • Standing behind counters or operating in staff-only zones
  • Serving customers for extended periods
  • Repeated movement paths within the store

This does not require staff to wear anything or press anything. It works entirely in the background.

Validation Workspace

There is a built-in workspace that allows you to:

  • Review thumbnails of detected staff and non-staff
  • Confirm or correct the system’s understanding
  • Train the environment once, and let it adapt over time

The system refines itself within minutes using these inputs. This ensures the results remain accurate as staff behaviour changes.

Validation Workspace

Risk Map

The system automatically builds a risk map, showing:

  • Where staff usually walk
  • Where customers typically move
  • Where uncertainties still exist

This helps you understand how the system makes decisions and where adjustments may be needed.

Risk Map

What You Can Expect

  • No extra hardware
  • No badges or tags
  • No staff training required
  • Works with existing cameras
  • Improves accuracy over time
  • Suitable for most retail environments

Small or highly constrained entrances may require additional camera coverage for best results.

Case Study

Luxury Boutique
Jewellery Store
Electronics Store
Cosmetics Retailer
Home Furnishing Store

Luxury Boutique

Case Study 1

Luxury Boutique – No Uniform, No Problem

Before

A luxury fashion boutique had no staff uniforms, making it impossible to distinguish staff from customers. Staff often moved around the shop floor helping VIP clients, and their movements inflated the customer counts significantly.

After

With Pro2 (2026) covering the entrance and the in-store cameras mapping movement patterns, the system learnt staff behaviour within minutes, who usually stands at the fitting rooms, who stays behind the counter, and who accompanies customers for long periods.

Outcome

The boutique finally obtained clean customer counts without changing staff dress codes.

Website takeaway

Perfect for luxury boutiques with no uniforms — the system recognises staff by behaviour, not clothing.

Jewellery Store

Case Study 2

Middle Eastern Jewellery Store – Similar Clothing for Everyone

Before

In a Middle Eastern jewellery store, both staff and customers frequently wore similar black or white attire. Traditional colour-based methods were ineffective, and manual tagging wasn’t practical.

After

The AI used travel paths and dwell zones, especially behind the consultation counters and display cases, to distinguish staff from customers. Heat maps showed clear staff-only zones, making exclusions accurate even when attire looked identical.

Outcome

The store gained reliable traffic data for the first time without any operational changes.

Website takeaway

Works even when staff and customers dress similarly, behaviour and position provide the clarity.

Electronics Store

Case Study 3

High-Street Electronics Store – Large Floor Area, Constant Movement

Before

The store struggled because staff moved constantly across the shop to assist customers, making them appear as new visitors each time they passed an entrance camera.

After

Thanks to the extended coverage of Pro2 (2026) and multiple interior cameras, the system tracked movement patterns across the entire shop floor. Staff who patrolled zones or repeatedly crossed the same aisles were automatically identified and excluded.

Outcome

Footfall accuracy improved instantly, and the store finally had reliable conversion rates.

Website takeaway

Ideal for busy stores where staff are always on the move — consistent behaviour patterns make staff easy to exclude.

Cosmetics Retailer

Case Study 4

Premium Cosmetics Retailer – Small Store, High Staff Engagement

Before

Staff frequently stood outside the storefront to welcome walk-in customers or demonstrate products. Because staff stood within the camera’s field of view, counts were heavily inflated.

After

With both entrance and in-store cameras contributing to the risk map, the system identified these recurring dwell spots as “staff zones”. Staff were excluded automatically even when they stood in customer-facing positions.

Outcome

The retailer could finally compare true walk-in traffic against sales and campaign performance.

Website takeaway

Great for beauty and cosmetics stores where staff engage customers in prominent areas.

Home Furnishing Store

Case Study 5

Home Furnishing Store – External Contractors and Mixed Roles

Before

The store frequently had stock replenishment contractors, interior stylists, and part-time staff entering and exiting the floor. This made it difficult to maintain consistent visitor numbers because the system couldn’t tell who was staff and who wasn’t.

After

The staff exclusion engine built a risk map showing high-frequency staff movement and shelf-restocking behaviour. Even new contractors were recognised by their dwell patterns and repeated presence in staff-only zones.

Outcome

The store gained stable traffic data without needing badges or manual tagging for contractors.

Website takeaway

Reliable for stores with mixed teams and contractors — repeated behaviour reveals who is staff.

Ready to learn more?

Next Steps

Selecting the appropriate functions and devices to serve your specific purpose is crucial. To get started with your own designs, it can be helpful to review deployments of similar systems in other malls for inspiration.

Talk to an Expert

Talk to one of our FootfallCam consultants to identify your requirements, budgets, and possibly conduct a Proof of Concepts, to help ensure that FootfallCam system is the right fit for your mall.