AI and IoT-Based Frameworks for Real-Time Crowd Monitoring and Security

Abstract

Effective crowd management is critical for ensuring public safety during large-scale events and in densely populated urban environments. Recent advances in deep learning and computer vision have enabled real-time crowd behavior analysis, including the detection of abnormal actions such as pushing, which can lead to dangerous situations. This paper presents a review of cloud-based deep learning frameworks, focusing on the use of convolutional neural networks (CNN) and optical flow models for early detection of pushing behavior in crowded event entrances. We discuss the integration of pre-trained deep models with live video stream processing to achieve high accuracy and low latency. Existing datasets and evaluation metrics are examined, with reported detection accuracies reaching up to 87%. The review also highlights challenges such as data privacy, real-time processing constraints, and the need for comprehensive models that consider multiple behavioral and environmental factors. Finally, future directions are proposed for developing autonomous crowd safety systems that mimic human situational awareness in complex urban settings.