AI-Driven Crowd Density Monitoring and Stampede Risk Alert System

Objective: Develop a real-time predictive platform to help local police and disaster management authorities minimize stampede risks during large public gatherings.

Problem: India hosts thousands of large-scale public events—religious festivals, processions, political rallies, and melas—where crowd surges and unplanned movements cause deadly stampedes, endangering lives and overwhelming local police and disaster response teams. This issue directly impacts public safety and aligns with the mandates of the Ministry of Home Affairs and SDG 16 (Peace, Justice & Strong Institutions).

Solution: The proposed solution is a deployable digital platform that leverages AI, computer vision, and open geospatial data to monitor crowd densities in real time via CCTVs and smartphones. It analyzes movement patterns, predicts congestion and risk zones, and issues early warnings to authorities for targeted interventions.

Key Features/Deliverables: The prototype will include: (1) a dashboard for real-time crowd heatmaps and density trends using video feeds and crowd-sourced mobile data, (2) an AI model to predict stampede risk based on live movement/flow, (3) alerting tools for authorities, and (4) simulation and analytical reports for planning safer events. The solution will use anonymized data, respecting privacy norms.

Impact and Scaling: By enabling early, data-driven interventions, the platform can substantially reduce casualties in high-risk events, build trust in civic infrastructure, and provide a scalable, low-cost tool for police, disaster management units, and municipal bodies across states. The open-data foundation allows for easy adaptation to various event types and geographies.