Abstract
Deep neural networks (DNNs) have revolutionized video surveillance systems by enabling advanced capabilities in object detection, tracking, and behavior analysis. This article explores the role of deep learning, particularly deep neural networks, in enhancing video surveillance systems. The applications of DNNs in real-time monitoring, anomaly detection, facial recognition, and crowd behavior analysis are discussed, demonstrating their potential to improve security and operational efficiency. The article also addresses challenges in implementing DNNs, including data privacy concerns, computational requirements, and ethical considerations.Deep Neural Networks
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