• Rushikesh Matele
• Atharva Patil
• Sanket Kshirsagar
• Prof.Babasaheb Waghmode

In today’s rapidly evolving technological landscape, ensuring public safety in crowded spaces has become a critical concern. This study presents an advanced Suspicious Activity and Crowd Monitoring Alert System designed to enhance security measures in various public environments. Leveraging cuttingedge technologies such as artificial intelligence and machine learning, the system provides real-time video surveil lance and intelligent analysis capabilities. By employing sophisticated algorithms, the system can differentiate between normal crowd behavior and suspicious activities, enabling prompt detection and response to potential threats. The integration of behavioral analytics and anomaly detection techniques further enhances the system’s accuracy in identifying abnormal patterns. The system generates instant alerts when suspicious activities are detected, facilitating swift actions by security personnel and law enforcement agencies. Additionally, the system ensures compliance with data privacy regulations and emphasizes responsible data handling practices. Through comprehensive literature review, this research explores the evolution of crowd monitoring systems, highlighting key challenges, ethical considerations, and emerging trends in the field. The results of this study contribute to the ongoing efforts to create safer public spaces, thereby enhancing overall public safety and security.

Suspicious and Crowd Monitoring, Public Safety, Security System, Machine Learning (ML), Real-time Monitoring, Threat Detection, Alert System, Crowd Management, Rapid Response, Public Events, Surveillance, Risk Mitigation,

"Crowd Monitoring and Alert System ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.f124-f128, April-2024, Available :http://www.jetir.org/papers/JETIR2404515.pdf

"Crowd Monitoring and Alert System ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppf124-f128, April-2024, Available at : http://www.jetir.org/papers/JETIR2404515.pdf