AI-Driven Approach to Real Time Crowd Monitoring and Risk Control

Anju Devi A, Jogimol Joseph

Abstract: AI-Driven Crowd Safety is an advanced Artificial Intelligence (AI)-based solution designed to monitor, analyze, and control crowd density in public spaces such as transportation hubs, stadiums, shopping malls, and event venues to ensure safety and prevent overcrowding-related hazards. With the rapid growth of urban populations and large-scale public gatherings, efficient crowd monitoring has become a critical requirement for public safety and disaster prevention. The proposed system leverages deep learning techniques, specifically the YOLO (You Only Look Once) object detection algorithm, for accurate and real-time head detection and crowd counting from image and video streams. By processing visual data captured through surveillance cameras, the system identifies individuals and estimates crowd density with high precision even in complex and dynamic environments. Unlike traditional crowd monitoring methods that rely heavily on manual supervision and are often time-consuming, inefficient, and prone to human error, the AI-Driven Crowd Safety provides a fully automated solution for crowd analysis. The system categorizes crowd density into three levels: low, medium, and high, based on predefined thresholds. When the crowd density exceeds safe limits, the system generates instant alerts to authorities, enabling timely intervention and effective crowd control measures. In addition to detection and alert generation, the system supports real-time monitoring and data visualization, allowing administrators to track crowd patterns and make informed decisions. The proposed solution is scalable, cost-effective, and adaptable to various environments, making it suitable for smart city applications and public safety management systems. By integrating AI-driven detection, automated analysis, and real-time alert mechanisms, the Crowd Management System significantly enhances situational awareness, reduces risks associated with overcrowding, and contributes to safer and more efficient management of public spaces.

Keywords: AI-Driven Crowd Safety, Artificial Intelligence, YOLO, Head Detection, Crowd Density Analysis, Deep Learning, Real-Time Monitoring, Alert System, Smart Surveillance

How to Cite?: Anju Devi A, Jogimol Joseph, "AI-Driven Approach to Real Time Crowd Monitoring and Risk Control", Volume 15 Issue 4, April 2026, International Journal of Science and Research (IJSR), Pages: 1375-1378, https://www.ijsr.net/getabstract.php?paperid=SR26421121714, DOI: https://dx.dx.doi.org/10.21275/SR26421121714

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