| # Crowd Intelligence | |
| Deployed link: https://crowd-intelligence-l58l.vercel.app/ | |
| A high-performance visual pedestrian and traffic tracking system specifically designed for robust crowd density calculation. Built for scale, it handles intense scenarios like the **Shibuya Scramble Crossing** by automatically extracting trajectories, dwell times, and spatial flow vectors. | |
| ## Cloud-Ready Architecture | |
| - **Backend:** FastAPI, YOLOv8 object detection, and ByteTrack with FFmpeg processing pipeline. | |
| - **Frontend:** React + Tailwind CSS v4, dynamic components via Framer Motion, and synchronous real-time Area/Pie charts using Recharts. | |
| ## Supported Metrics | |
| - **Unique Pedestrians Tracking**: Tracks people across frames despite occlusions. | |
| - **Dwell Time Calculation**: Estimates the average seconds a target remains in frame. | |
| - **Flow Velocity & Vectors**: Maps directional movement (`Left -> Right`, `Top -> Bottom`). | |
| - **Density Over Time**: Provides frame-by-frame data points charting crowd compression levels. | |
| ## Deployment Instructions | |
| ### Vercel (Frontend) | |
| 1. Import repository to Vercel. | |
| 2. Root directory: `frontend/` | |
| 3. Framework Preset: `Vite` | |
| 4. Important Environment Variable: | |
| - `VITE_API_URL` = `YOUR_RENDER_BACKEND_URL` | |
| ### Render (Backend AI) | |
| 1. Connect this repo to Render as a New Web Service. | |
| 2. Select Environment: **Docker** (Uses the included `Dockerfile` containing OpenCV/FFmpeg system graphics dependencies). | |
| 3. (Suggested constraint): Default model is YOLOv8m. Due to Free Tier memory constraints, edit `api.py` or your configs to use `yolov8n.pt` if you experience OOMKilled errors. | |
| ## Local Development | |
| **Run Backend:** | |
| ```bash | |
| python3 -m uvicorn api:app --host 0.0.0.0 --port 8001 | |
| ``` | |
| **Run Frontend:** | |
| ```bash | |
| cd frontend | |
| npm install | |
| npm run dev | |
| ``` | |
| Project developed for structural crowd analytics. | |