Crowd Dynamics Analysis

Abstract

Understanding crowd dynamics in densely populated public spaces, such as city centers, stadiums, and transit hubs, is vital for ensuring public safety and efficient management. The complexities of crowded environments introduce various challenges, including traffic congestion, overcrowding, and potential safety hazards. Traditional methods of crowd analysis often fall short of providing comprehensive insights, relying on human observation or outdated sensor technologies. However, recent advancements in artificial intelligence, particularly using generative adversarial networks, opened new avenues for studying crowd behavior and density in real-time video feeds. The integration of GAN-based crowd analysis not only offers real-time monitoring but also enables the anticipation of potential safety hazards before they escalate. The chapter delves into the various applications of GANs in crowd behavior analysis, anomaly detection, and intelligence provision to security personnel.