LEVERAGING DEEP LEARNING FOR ADVANCED VIDEO SURVEILLANCE AND REAL-TIME ANALYSIS

Abstract

In recent years, the application of deep learning techniques in video surveillance systems has revolutionized the way security and monitoring are conducted. Traditional surveillance systems are limited by human intervention and static analysis, making them inefficient for large-scale or real-time monitoring. The integration of deep learning algorithms, particularly Convolutional 
Neural Networks (CNNs), has enabled the development of intelligent video surveillance systems capable of performing tasks such as 
object detection, face recognition, activity monitoring, and anomaly detection with high accuracy and speed.