Drishti AI-Event Guardian: An Intelligent Real-Time Crowd Monitoring and Emergency Response System for Mass Gathering Events
arXiv:2606.05185v1 Announce Type: cross Abstract: Mass gathering events are associated with critical safety incidents caused by insufficient crowd monitoring and inadequate emergency response coordination. Traditional surveillance systems lack intelligent analytics, resulting in delayed threat identification, poor resource deployment, and weak support for vulnerable individuals during dense public assemblies. This paper presents Drishti AI-Event Guardian, an intelligent crowd management framework using deep learning for public safety enhancement. The architecture combines multimodal data from
The increasing sophistication and accessibility of deep learning and multimodal data processing enable the development of advanced real-time monitoring systems for public safety.
This development represents a significant step towards leveraging AI for enhancing public safety and security, offering solutions for critical crowd management scenarios.
Traditional surveillance is augmented by intelligent, AI-driven analytics, shifting from reactive to proactive threat identification and emergency response in mass gatherings.
- · Public safety agencies
- · Event organizers
- · AI surveillance companies
- · Deep learning researchers
- · Traditional surveillance system providers
- · Manual crowd management services
- · Organizations with inadequate safety protocols
Mass gathering events become safer and more efficiently managed due to intelligent real-time monitoring.
Increased adoption of AI in urban planning and smart city initiatives for security and operational efficiency.
Potential ethical and privacy debates escalate regarding pervasive AI surveillance in public spaces.
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