Pose Estimation and Threat Identification : A Computer Vision Approach For Enhanced Surveillance Systems | ||||
| Alexandria Journal of Science and Technology | ||||
| Article 10, Volume 3, Issue 1 - Serial Number 5, June 2025, Page 91-98 PDF (1.23 MB) | ||||
| Document Type: Research article | ||||
| DOI: 10.21608/ajst.2025.372567.1069 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Rashad M. Sallam  ; Ahmed M. A. El-Sayed; Nermeen M Kashief
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| Mathematics and Computer Science Department, Faculty of Science, Alexandria University, Alexandria, Egypt. | ||||
| Abstract | ||||
| Abstract— In today’s security landscape, traditional surveil- lance systems struggle with inefficiencies such as operator fatigue, missed threats, and delayed responses. To address these challenges, we propose a Smart Policing Surveillance System leveraging advanced computer vision and deep learning. Our system integrates human pose estimation, gesture analysis, and weapon detection to identify both visible and concealed threats. By analyzing body language and movements, it detects suspi- cious behavior such as the positioning of arms and identifies victims in distress through gestures like raised hands. At its core, the system employs pose estimation for precise body tracking and deep learning models for real-time threat detection. Focusing on critical areas like hand positions, it re- duces computational overhead while maintaining high accuracy. Designed to perform reliably in challenging conditions such as crowded environments, it triggers real-time alerts upon threat detection, enabling swift responses from law enforcement. Our experimental results demonstrate the system’s effective- ness: it achieves 94.3% accuracy in pose estimation, 97.7% weapon detection rate, and identifies raised hands with 89.8% precision. The integrated solution detects more threats than conventional systems while reducing false alarms . In field tests, the system demonstrated faster threat identification compared to human operators, proving its value as a vital tool for modern security challenges. | ||||
| Keywords | ||||
| Weapon Detection; Human Gesture Analysis; Deep Learning; CCTV cameras , Real-time Alert Syststem | ||||
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