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

2024/03/31 by Abdul Aziz A. B, B, Abdul Aziz A., Aindri Bajpai +1
Computer Science · Engineering · #68T05 #68T40 #68T45 #68U20 #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Network Security and Intrusion Detection #Smart Grid Security and Resilience

paper · pdf · doi:10.48550/arxiv.2404.00645

openalex publication_date 2024/03/31 · openalex created_date 2024/04/04 · openalex updated_date 2026/07/28

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.

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