• Tanuja Sandip Shelke
• Sejal Dinesh Shinde
• Sharvari Sandip Gaikwad

Overcrowding in public spaces like stations, malls, stadiums, and religious gatherings increases risks such as stampedes, delays, and evacuation challenges. Manual monitoring via CCTV and personnel is often slow and error-prone. To address this, we propose a hybrid Crowd Density Management and Monitoring System (CDMMS) combining AI and IoT. Surveillance video is processed with Florence-2 and OpenCV for crowd detection, while IoT sensors provide device-based counting. An LSTM-based module predicts crowd flow, enabling proactive management. When thresholds are exceeded, alerts, smart displays, and servo-controlled barriers guide safe movement. Experiments show 92% accuracy and a 27% congestion reduction, making CDMMS a scalable solution for smart cities and event management.

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"Crowd Density Management and Monitoring Using AI and IoT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 9, page no.d563-d568, September-2025, Available :http://www.jetir.org/papers/JETIR2509376.pdf

"Crowd Density Management and Monitoring Using AI and IoT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 9, page no. ppd563-d568, September-2025, Available at : http://www.jetir.org/papers/JETIR2509376.pdf