Attire-Based Anomaly Detection in Restricted Areas Using YOLOv8 for Enhanced CCTV Security

TL;DR

This paper presents a novel security system that uses YOLOv8 for attire-based anomaly detection in CCTV footage to identify unauthorized individuals in restricted areas, combining advanced image analysis with soft computing for improved accuracy.

Contribution

It introduces a new approach integrating YOLOv8 and soft computing techniques for attire-based anomaly detection in surveillance systems, enhancing security in restricted zones.

Findings

High accuracy in detecting unauthorized attire

Effective performance under varying lighting conditions

Potential for real-time security monitoring

Abstract

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.