Advancements in Crowd Analysis: Datasets and Deep Learning Approaches for Enhanced Surveillance and Management

The study explores crowd control in densely populated metropolitan settings, emphasizing the necessity of striking a compromise between convenience and security. It looks at communication strategies, crowd management techniques, and emergency readiness. It also looks at the function of smart surveillance systems, which study crowd data and behaviors, particularly for anomaly detection. The study addresses the difficulties associated with crowd estimation in populated areas and suggests deep learning methods to improve precision. It makes a distinction between crowd management and control, emphasizing the part data analytics plays in urban dynamics. There is a discussion of how urbanization affects crowd gatherings and a plea for effective crowd control to avoid chaos. The project integrates technology with health management for huge events, advancing automated surveillance and crowd control. It emphasizes how feature alignment and semi-supervised learning can be used to handle domain adaptability in crowd counting. In this research survey, we explore real-time object detection using the YOU ONLY LOOK ONCE (YOLO) algorithm and convolutional neural networks (CNNs).The goal of the study is to create safer and more ordered urban areas by making a significant contribution to computer vision and crowd control.