Surveillance System

Modern surveillance systems aim to enhance safety and security through automated video analysis, focusing on anomaly detection and recognition of suspicious activities. Research heavily utilizes deep learning architectures like convolutional and recurrent neural networks, graph neural networks, and object detection models (e.g., YOLOv8), often incorporating multimodal data and addressing challenges like imbalanced datasets and real-time processing constraints. These advancements are impacting various sectors, including transportation, public spaces, and security, by enabling more efficient and accurate monitoring while also raising important ethical considerations regarding privacy and bias in AI algorithms. The field is actively exploring robust solutions to address adversarial attacks and improve the reliability and ethical deployment of these systems.