You can deploy this model and start earning money today!

Abhisingh-18/
Crowd-Intelligence

Views

No views yet

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)

Import repository to Vercel.
Root directory: frontend/
Framework Preset: Vite
Important Environment Variable:
- VITE_API_URL = YOUR_RENDER_BACKEND_URL

Render (Backend AI)

Connect this repo to Render as a New Web Service.
Select Environment: Docker (Uses the included Dockerfile containing OpenCV/FFmpeg system graphics dependencies).
(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:

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.

Abhisingh-18/Crowd-Intelligence