Neural Networks for Advanced Video Analytics in Surveillance

Abstract

The integration of neural networks in video analytics is transforming the field of surveillance, enabling automated monitoring, detection, and response to security events in real-time. This article explores the role of neural networks, especially deep learning models, in advanced video analytics for surveillance systems. We discuss key applications such as object detection, facial recognition, behavior analysis, and anomaly detection, emphasizing the benefits of AI in enhancing surveillance capabilities. We also address challenges such as data privacy, model interpretability, and system robustness, and examine the future potential of neural networks in improving surveillance technologies.