Inspiration
Inspiration
Managing large crowds in public places is a major challenge and can lead to accidents or unsafe situations. We were inspired to create a smart system that can monitor crowd density in real time and help prevent overcrowding before it becomes dangerous.
What it does
Our Smart Crowd Management System uses AI and IoT to detect, count, and analyze people in real-time using camera feeds. It calculates crowd density and sends instant alerts when overcrowding is detected. The system also suggests alternative routes to reduce congestion and improve movement flow.
How we built it
We used Python with OpenCV and YOLO for real-time people detection and counting. The system processes live video feeds, sends data to a backend server built with Flask/Node.js, and displays insights on a web dashboard. IoT devices like Raspberry Pi/Arduino can be used for deployment and data collection.
Challenges we ran into
One of the biggest challenges was maintaining accuracy in crowded environments where people overlap. Lighting conditions and real-time processing speed also required optimization.
Accomplishments that we're proud of
We successfully built a working prototype that can detect and count people in real time and generate alerts when crowd density exceeds a threshold.
What we learned
We learned how to apply computer vision in real-world scenarios, integrate AI with IoT systems, and build scalable real-time monitoring solutions.
What's next for Smart Crowd Management System
We plan to enhance the system with AI-based crowd prediction, mobile app integration, live heatmaps, and smarter traffic/crowd control systems.
Log in or sign up for Devpost to join the conversation.