Occupancy, density and dwell-time analytics

The problem

How it works

1. 1Zones are defined on camera views.
2. 2People are detected and counted per zone.
3. 3Dwell time and heatmaps are calculated over time.
4. 4Alerts fire when occupancy exceeds configured limits.

What can be detected

• Live occupancy per zone
• Capacity limit exceeded
• Average dwell time
• Heatmaps of busy areas

Typical scenarios

• Store layouts and promotions
• Restaurants and lobbies
• Canteens and break areas
• Assembly points and exhibition halls

Working with existing cameras

• Wide views with limited occlusion work best; dense crowds reduce counting precision.
• 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 reports
• REST API to BI or building-management systems

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

• Density estimates in very crowded scenes are approximate.
• No identification of individuals is performed.