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Crowd Dynamics Analysis: GAN-Powered Insights for Enhanced Public Safety
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Author(s): Harshita Chourasia (G.H. Raisoni College of Engineering, India), Neha Tiwari (Oriental Institute of Science and Technology), Shraddha Raut (G.H. Raisoni University, Amravati, India), Anansingh Thinakaran (G.H. Raisoni College of Engineering, India)and Anirudh A. Bhagwat (G.H. Raisoni College of Engineering, India)
Copyright: 2024
Pages: 13
Source title:
Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs)
Source Author(s)/Editor(s): Sivaram Ponnusamy (Sandip University, Nashik, India), Jilali Antari (Ibn Zohr Agadir University, Morocco), Pawan R. Bhaladhare (Sandip University, Nashik, India), Amol D. Potgantwar (Sandip University, Nashik, India)and Swaminathan Kalyanaraman (Anna University, Trichy, India)
DOI: 10.4018/979-8-3693-3597-0.ch007
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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methods of crowd analysis often fall short of providing comprehensive insights, relying on human observation or outdated sensor technologies.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.
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