AI Enhanced Surveillance for Identifying and Recognizing Crowd Behavior

The application of deep learning methods in AI-powered surveillance systems for detecting and interpreting crowd behavior. It reviews the latest progress in computer vision and neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers—essential for tasks such as estimating crowd density, recognizing activities, and detecting anomalies. The discussion covers both supervised and unsupervised learning strategies, domain adaptation through transfer learning, and the integration of multiple data sources to enhance accuracy and system resilience. It also emphasizes the impact of hardware advancements, like GPUs and edge computing, in enabling real-time analysis. Furthermore, the paper addresses key challenges, including limited data availability, lack of model transparency, and inherent biases in crowd behavior datasets. This comprehensive survey aims to deepen understanding in the field and support the development of more efficient, reliable, and ethically sound crowd monitoring solutions