Title:
Deep Learning-based Person Tracking using Facial Recognition: A Smart Approach to Security and Civic Monitoring
Authors:
Shailendra Singh Kathait, Ashish Kumar, Samay Sawal
Summary:
Person tracking using facial recognition has emerged as a crucial technology in surveillance, security, and human-computer interaction applications. This paper presents a comprehensive framework that integrates advanced facial detection, feature extraction, and tracking methodologies to robustly identify and monitor individuals in video streams. The approach in this paper combines state-of-the-art computer vision techniques with deep learning-based facial recognition to achieve real-time performance while maintaining high accuracy. The system integrates YOLO for object detection and DeepFace for facial recognition, offering an efficient solution for real-time person tracking. Additionally, the framework extends beyond individual tracking by incorporating intelligent analysis for detecting traffic violations, monitoring criminal activities, and identifying civic issues such as unauthorized encroachments or safety hazards. By leveraging existing surveillance infrastructure, this system enhances preventive policing and response times, making urban spaces safer and more efficient. The system is built using widely available open-source libraries and is designed for scalability across various camera setups. Experimental results demonstrate that this framework provides effective tracking and identification even under challenging conditions such as occlusions, varied lighting, and rapid movements.
