Deep Learning for Real-Time Surveillance and Anomaly Detection

Abstract

Real-time surveillance and anomaly detection have become critical for ensuring safety and security in various applications, such as public spaces, critical infrastructure, and healthcare facilities. Traditional systems, relying on manual intervention and rule-based algorithms, often struggle with the dynamic and vast nature of video data. Recent advancements in deep learning have paved the way for more efficient, automated, and accurate detection systems. This paper explores the application of deep learning techniques, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in real-time surveillance and anomaly detection. The paper highlights the potential of these models to not only enhance detection accuracy but also offer scalable and robust solutions. The challenges, methods, and future directions of deep learning in this field are also discussed.