Attire-Based Anomaly Detection in Restricted Areas Using YOLOv8 for
  Enhanced CCTV Security
Abdul Aziz A. B Aindri Bajpai
This research introduces an innovative security enhancement approach,
employing advanced image analysis and soft computing. The focus is on an
intelligent surveillance system that detects unauthorized individuals in
restricted areas by analyzing attire. Traditional security measures face
challenges in monitoring unauthorized access. Leveraging YOLOv8, an advanced
object detection algorithm, our system identifies authorized personnel based on
their attire in CCTV footage. The methodology involves training the YOLOv8
model on a comprehensive dataset of uniform patterns, ensuring precise
recognition in specific regions. Soft computing techniques enhance adaptability
to dynamic environments and varying lighting conditions. This research
contributes to image analysis and soft computing, providing a sophisticated
security solution. Emphasizing uniform-based anomaly detection, it establishes
a foundation for robust security systems in restricted areas. The outcomes
highlight the potential of YOLOv8-based surveillance in ensuring safety in
sensitive locations.