Integrating Object Detection and Advanced Analytics for Smart City Crowd Management

In the context of rapidly advancing smart cities, efficient crowd analysis plays a crucial role in ensuring public safety, urban planning, and resource management. This paper presents a novel framework that combines the popular You Only Look Once (YOLO) object detection algorithm with advanced crowd analysis techniques, aiming to improve the understanding and management of urban crowd dynamics. The proposed framework leverages YOLO's real-time object detection capabilities to detect various objects within video frames, with a particular focus on identifying individuals. To initiate the crowd analysis process, the detected persons are isolated and tracked over time, enabling the collection of valuable data for comprehensive crowd behavior analysis. By leveraging this rich dataset, the framework enables the extraction of key crowd characteristics, such as crowd density, crowd flow patterns, crowd distribution, and crowd congestion levels. Moreover, the framework incorporates techniques to analyze the extracted data, offering valuable insights into crowd dynamics.