
AI-powered stampede prevention through real-time crowd analysis
RakshaNet is a Flask-based video analytics system that monitors crowd density, flow patterns, and chaos levels in real-time to detect dangerous crowd conditions before stampedes occur, using YOLO detection, optical flow analysis, and a dual-gate risk model.
During religious festivals, transportation hubs, and public events, overcrowded spaces can turn deadly within seconds when too many people push in conflicting directions. Security staff watching camera feeds often cannot tell the difference between a calm but packed queue and a dangerous crush until people start falling. By the time panic is visible, it is too late to prevent injuries or deaths. Organizers need early warnings that account for both how tightly people are packed and whether the crowd is moving chaotically, so they can hold entry gates or open alternate exits before a stampede begins.
RakshaNet addresses the challenge of managing overcrowding at public events by providing real-time crowd analysis and alerting systems. By leveraging advanced computer vision techniques to analyze video feeds, it detects potential risks before they escalate into dangerous situations, thereby enabling event organizers to take preventive measures to protect attendees.
This isn't just a project — it's a simulation of how software actually gets built inside a company. You'll see how real teams gather a client's requirements, negotiate trade-offs, and ship in sprints instead of all at once, while picking up the daily rhythm of an engineer's (and tester's) week along the way.
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