Anomaly Detection in Behavioral Surveillance Using Deep Learning Techniques

This chapter explores techniques for detecting abnormal behavior in surveillance systems using deep learning. Modern surveillance relies on advanced models to identify suspicious activities crucial for safety and monitoring. The discussion traces the evolution from traditional rule-based methods to deep models such as convolutional and recurrent neural networks, autoencoders, and transformers. It highlights challenges in defining normality, managing temporal variations, handling large-scale data, and ensuring privacy. The chapter examines temporal behavior models, including LSTM encoders, 3D CNNs, and attention-based architectures. Real-world applications in video surveillance, crowd monitoring, and human activity analysis are reviewed. Issues like data imbalance, real-time inference, model explainability, and ethics are discussed. Finally, performance metrics, evaluation protocols, and model comparisons are outlined to guide readers in designing effective anomaly detection frameworks for practical use.