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c3c6f00 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | # 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.
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