Chapter 3: Machine Learning and Deep Learning Algorithms in Surveillance Systems

Machine learning (ML) and deep learning (DL) have been leveraged in surveillance systems to change the way of threat identification, criminal prevention, and public safety monitoring. In this chapter, we give an overview of related machine learning and DL techniques, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer‑based architectures, as well as the use of them to solve problems in image and video surveillance. Then it looks at how precise these models can make item identification, facial recognition, and activity recognition. Real‑time inference in surveillance applications is also covered in the chapter, as well as the training datasets and the model optimization strategies. Also, it looks into the issue of computing cost, data privacy, and the ethics of AI‑powered monitoring. It presents here extensively the impact of ML and DL algorithms in bringing forth forthcoming generations of intelligent security solutions.