Enhanced people re-identification in CCTV surveillance using deep learning: a framework for real-world applications

People re-identification (ReID) plays a pivotal role in modern surveillance, enabling continuous tracking of individuals across various CCTV cameras and enhancing the effectiveness of public security systems. However, ReID in real-world CCTV footage presents challenges, including changes in camera angles, variations in lighting, partial occlusions, and similar appearances among individuals. In this paper, we propose a robust deep learning framework that leverages convolutional neural networks (CNNs) with a customized triplet loss function to overcome these obstacles and improve re-identification accuracy. The framework is designed to generate unique feature embeddings for individuals, allowing precise differentiation even under complex environmental conditions. To validate our approach, we perform extensive evaluations on benchmark ReID datasets, achieving state-of-the-art results in terms of both accuracy and processing speed. Our model's performance is assessed using key metrics, including Cumulative Matching Characteristic (CMC) and mean Average Precision (mAP), demonstrating its robustness in diverse surveillance scenarios. Compared to existing methods, our approach consistently outperforms in both accuracy and scalability, making it suitable for integration into large-scale CCTV systems. Furthermore, we discuss practical considerations for deploying AI-based ReID models in surveillance infrastructure, including system scalability, real-time capabilities, and privacy concerns. By advancing techniques for re-identifying people, this work not only contributes to the field of intelligent surveillance but also provides a framework for enhancing public safety in real-world applications through automated and reliable tracking capabilities.