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Advanced Cyber-Physical Systems Utilizing Deep Learning for Crowd Density Detection and Public Safety
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Author(s): R. Leisha (Christ University, India), Katelyn Jade Medows (Christ University, India), Michael Moses Thiruthuvanathan (Christ University, India), S. Ravindra Babu (Christ University, India), Prakash Divakaran (Himalayan University, India)and Vandana Mishra Chaturvedi (D.Y. Patil University, India)
Copyright: 2025
Pages: 40
Source title:
Navigating Cyber-Physical Systems With Cutting-Edge Technologies
Source Author(s)/Editor(s): Ramesh Chandra Poonia (Christ University, India)and Kamal Upreti (Christ University, India)
DOI: 10.4018/979-8-3693-5728-6.ch004
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Abstract
This study aims to detect the increasing crowd density, which is crucial, especially in dynamic environments like festivals or concerts. By harnessing cyber-physical systems and cutting-edge technologies, we have employed computer vision and deep learning to create a reliable model for accurate crowd counting. Utilizing deep learning, renowned for its ability to handle image-related tasks, the system gives decision-makers precise crowd density estimates, enabling well-informed actions such as crowd control measures. This study aims to improve safety and security in crowded areas by delivering an efficient system that can identify and quantify high crowd densities by integrating deep learning and computer vision technologies within a cyber-physical system framework. This approach facilitates proactive measures to mitigate safety risks and optimize crowd management strategies. The study seeks to advance safety and security measures in crowded areas by delivering an efficient and comprehensive crowd density detection and analysis system using cutting-edge technologies.
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