CrowdSense

Inspiration

Large public gatherings often face overcrowding, poor crowd management, and delayed emergency response, which can lead to accidents and stampedes. We wanted to build a proactive system that uses AI to improve public safety.

What it does

CrowdSense predicts crowd-related risks for events, analyzes crowd density from images/CCTV feeds, provides AI-generated safety recommendations, sends emergency alerts, and suggests safer evacuation routes.

How we built it

We built the frontend using React, TypeScript, and Vite. We integrated Google Gemini AI for risk analysis and recommendations. The system uses image-based crowd analysis, event data processing, and route planning concepts to support emergency management.

Challenges we ran into

Integrating AI responses effectively into the application. Designing realistic crowd-risk prediction logic. Implementing route planning and emergency workflows. Managing dependencies and deployment across different environments.

Accomplishments that we're proud of

Successfully integrated Gemini AI into the project. Built a functional crowd-risk prediction system. Designed a complete emergency response workflow. Created an intuitive and user-friendly dashboard.

What we learned

We learned how to integrate AI APIs into real-world applications, work with modern web technologies, manage projects using GitHub, and design scalable solutions for public safety problems.

What's next for CrowdSense

We learned how to integrate AI APIs into real-world applications, work with modern web technologies, manage projects using GitHub, and design scalable solutions for public safety problems.

Built With

• and
• built-with:-react
• express.js
• github
• google-gemini-api
• google-maps
• node.js
• tailwind-css
• typescript
• vite