The problem
Long queues cost sales and customer satisfaction, but branch and store managers often learn about them from complaints rather than data.
How it works
- 1Queue areas are defined on camera views at counters or checkouts.
- 2People in the area are detected and tracked over time.
- 3Queue length and estimated waiting time are calculated continuously.
- 4Threshold alerts and hourly patterns support staffing decisions.
Existing IP cameras / NVR
RTSP / ONVIF streams
Edge device on-site
AI models + rules; video stays local
Events
Time, camera, zone, evidence frame
Dashboard, alerts, API
Notifications, relays, REST/webhook
What can be detected
- Queue length above threshold
- Estimated waiting time
- Hourly and daily queue patterns
- Open counters versus queue load
Typical scenarios
- Bank branches
- Supermarket checkouts
- Restaurant order counters
- Hotel receptions and service desks
Deployment options
On-premise / edge
Analysis runs on hardware inside your network. Video does not leave the site; only events and metrics are stored or shared.
Cloud
Suitable for distributed, low-camera-count sites where central management matters more than keeping video on-site.
Hybrid
Video is processed on-site, while events, dashboards and multi-site reporting are managed centrally.
Working with existing cameras
- A view covering the whole queue area is needed; overhead or angled views both work if people are separable.
- Most IP cameras and NVRs that provide RTSP or ONVIF streams can be connected, regardless of brand.
- Before a pilot, sample footage from each candidate camera is reviewed for resolution, angle, distance and lighting.
- Where a view is not suitable, the recommendation is usually to reposition the camera or add one — not to replace the whole system.
Integration
- Dashboard and alerts
- REST API for workforce-management or BI tools
Privacy and security
- No face recognition is used in safety and operations analytics; events describe what happened, not who.
- On-premise processing keeps video inside your network.
- Evidence clips, retention periods and user access are configurable and should follow your GDPR/KVKK policies.
- Workplace monitoring should be introduced with clear employee information and, where applicable, consultation with employee representatives.
Limitations
- Very dense crowds reduce counting accuracy.
- No identification of individuals is required or performed.
How a pilot works
- 01
Discovery
We agree on the problem, the owner, the sites and what a successful outcome would look like.
- 02
Camera assessment
Sample footage from candidate cameras is reviewed for angle, resolution, distance and lighting.
- 03
Use-case definition
Events, zones, rules, alert recipients and success metrics are written down before deployment.
- 04
Pilot deployment
Edge hardware or cloud processing is set up and connected to the selected cameras.
- 05
Validation
Detections are checked against reviewed footage; true detections, false alarms and misses are counted per camera.
- 06
Report
Results, limitations and recommended changes are documented and shared with all stakeholders.
- 07
Rollout decision
Based on measured results, you decide whether and how to expand to more cameras, sites or use cases.
Frequently asked questions
Can it alert staff to open another counter?
Yes, threshold-based alerts can be sent to managers or displayed on a dashboard.
Does it recognise customers?
No. It counts people in an area; it does not identify them.
Related solutions
Start with the cameras you already have
Send us a few frames from your cameras and the problem you want to solve. We will tell you honestly what is feasible.