AI Software analytics, ML of Camera feed
We have implemented an advanced Headcount Entry–Exit Monitoring System for ADNOC facilities that provides real-time, camera-based monitoring of workforce movement. The system leverages AI-driven computer vision and deep learning algorithms to automatically recognize, track, and count individuals entering or leaving specific access points. Unlike conventional RFID or manual log systems, this solution uses live camera feeds to ensure high accuracy, zero manual intervention, and continuous visibility of site occupancy. Each entry and exit event is time-stamped and processed in real time, allowing ADNOC’s operations and security teams to maintain an accurate record of personnel presence across multiple sites.
Beyond basic counting, the system functions as a core facility-management and security intelligence tool. It integrates with centralized dashboards that visualize entry-exit statistics, heatmaps, and live occupancy levels for different zones. The platform can generate instant alerts in case of overcrowding, unauthorized entry, or emergency evacuation events, helping ADNOC enhance safety compliance and operational efficiency. Its user-friendly interface allows administrators to manage camera streams, compare analytics across buildings, and export reports for audits or manpower optimization. This AI-powered solution strengthens ADNOC’s commitment to innovation, safety, and data-driven workforce management.
Advantages:
- Provides accurate real-time headcount and occupancy tracking using AI and computer vision.
- Eliminates manual errors, ensuring reliable workforce monitoring and safety compliance.
- Integrates seamlessly with dashboards for alerts, analytics, and multi-location visibility.
Applications:
- Facility management and access control within industrial and corporate premises.
- Safety monitoring and evacuation management in high-risk or restricted zones.
- Workforce tracking and productivity analysis across ADNOC’s operational sites.