The Abu Dhabi Police Training Centre in Al Ain
encountered issues with disorganized processes,
leading to irregular attendance, lack of visitor count,
and an inability to gauge student emotions. This
hindered effective management of training sessions
and compromised student welfare.

The Abu Dhabi Police Training Centre in Al Ain plays a pivotal role in shaping the skills and capabilities of law enforcement
personnel. However, the center faced significant challenges in maintaining organization and monitoring student attendance and
well-being. To address these issues, a sophisticated video analytics solution was implemented, revolutionizing the training
center’s operations.

To remedy these challenges, a robust video analytics solution was
devised and implemented at the Abu Dhabi Police Training Centre in A
Ain. The solution comprised the following key features:
Automated Attendance Tracking: Leveraging CCTV cameras, the
system automated attendance tracking for each training session. It
dynamically managed student additions and deletions, ensuring
real-time and accurate attendance data.
Emotion Analysis: Advanced facial recognition technology was
employed to analyze students’ emotional states during sessions.
This feature provided valuable insights into student engagement
and well-being, enabling timely support interventions.
Visitor Monitoring: Existing CCTV infrastructure was utilized to
monitor visitor entry and exit, facilitating precise visitor count for
each day and enhancing overall security and resource
management.

The implementation of a deep learning-based solution revolutionized
operations at the Abu Dhabi Police Training Centre. Key aspects of the
implementation included:
Deep Learning Integration: A sophisticated deep learning solution was
deployed, seamlessly integrating with the existing CCTV cameras
within the network. This integration ensured comprehensive coverage
of the training center’s premises, enabling efficient monitoring and
analysis.
On-Premises Deployment: The solution was deployed on-site,
leveraging an edge AI device capable of connecting to 19 cameras
simultaneously. This on-premises deployment ensured real-time
processing of video feeds, minimizing latency and facilitating prompt
response to detected events.
Edge AI Device: The edge AI device served as the central processing
unit, equipped with powerful algorithms for attendance tracking and
emotion analysis. Its compact size and robust capabilities made it an
ideal solution for deployment at the training center, ensuring seamless
integration with existing infrastructure.
Dynamic Scalability: The solution was designed for dynamic
scalability, allowing for easy addition or removal of cameras as per the
center’s evolving needs. This scalability ensured adaptability to
changing requirements and future expansion.

The implementation of the video analytics solution at the Abu Dhabi Police Training Centre in Al Ain yielded significant benefits:
Enhanced Attendance Accuracy: Automated attendance tracking reduced manual errors, leading to a 20% improvement in
attendance record accuracy.
Improved Student Well-being: Emotion analysis tools detected stress or dissatisfaction in students, enabling authorities to
intervene promptly, resulting in a 15% increase in student satisfaction levels.
Efficient Resource Allocation: Visitor monitoring functionality provided insights into facility usage, resulting in a 25%
reduction in resource wastage through optimized resource allocation.
Informed Decision Making: Regular reports generated by the solution empowered authorities with comprehensive insights,
facilitating data-driven decision-making and process optimization.