SmartTrafficAnalyzer is a real‑time, intelligent traffic monitoring and analytics system. It uses AI/ML, computer vision, and streaming data to analyze vehicle flow, detect incidents, and provide insights to improve urban mobility and safety.
Traffic congestion, accidents, and inefficient traffic light timing are major challenges for modern cities. The goal of SmartTrafficAnalyzer is to provide a scalable platform for:
- Monitoring live traffic via camera feeds
- Analyzing vehicle counts, speeds, and traffic density
- Detecting anomalies (e.g., accidents, stalled vehicles)
- Visualizing traffic flows and trends
- Enabling data-driven decisions for traffic management authorities
- Real-time traffic video ingestion and preprocessing
- Vehicle detection / tracking using object detection
- Speed estimation and density calculation
- Incident and anomaly detection
- Dashboard for visualization and analytics
- Alerts or notifications when anomalies are detected
- Modular architecture to plug in new models, sensors, or analytics
Below is a high-level overview of how the system components interact:
Camera / Video Input → Ingestion & Preprocessing → Object / Vehicle Detection → Tracking & Analytics → Storage & Alerts → Dashboard / UI
Key modules:
- Ingestion / Streaming: Receives video frames, handles buffering
- Detection / Tracking: Performs object detection (cars, bikes, etc.) and tracks objects over frames
- Analytics: Computes traffic metrics (flow, speed, congestion), anomaly detection
- Backend / API: Exposes results, stores data
- Frontend Dashboard: Visual interface to monitor traffic, see alerts
- Node.js (>= 16.x recommended)
- npm or yarn
- Python & relevant ML / CV dependencies (if detection models are in Python)
- GPU support (optional, for accelerated detection)
- Camera or sample video feeds
-
Clone the repository:
git clone https://github.com/devloper-student/SmartTrafficAnalyzer.git cd SmartTrafficAnalyzer -
(Frontend) Install dependencies:
cd client npm install -
(Backend) Install dependencies:
cd ../server npm installOr if using Python for detection, install required packages (OpenCV, TensorFlow / PyTorch, etc.)
-
Shared / common modules:
cd ../shared npm install # or integrate as necessary
-
Create environment files:
.envConfigure variables like camera URLs, database credentials, API keys, etc.
In .env, example settings:
CAMERA_URL=rtsp://...
API_PORT=5000
DB_HOST=localhost
DB_USER=...
DB_PASS=...
Adjust according to your setup.
-
Start backend / detection server:
cd server npm start -
Start frontend dashboard:
cd ../client npm run dev -
Open browser on
http://localhost:3000(or whichever port you configured) to see live traffic metrics and dashboards.
- Add or configure camera feed sources
- Monitor real-time traffic metrics (vehicle count, speed, flow)
- Receive alerts/notifications on detected incidents
- View historical trends and analytics dashboards
(You can include screenshots or GIFs here to illustrate the UI and visualizations)
SmartTrafficAnalyzer/
├── client/ # Frontend dashboard (TypeScript, React, etc.)
├── server/ # Backend + detection / analytics
├── shared/ # Shared utilities, types, modules
├── .env # Configuration (not committed)
├── .gitignore
├── package.json
├── tsconfig.json
└── README.md
You can expand this depending on where model files, datasets, or utilities reside.
- Frontend: React, TypeScript, Tailwind CSS
- Backend / API: Node.js / Express (or similar)
- Computer Vision / ML: OpenCV, TensorFlow or PyTorch, object detection & tracking
- Database / Storage: PostgreSQL, TimescaleDB, or NoSQL depending on data needs
- Streaming / Messaging: Kafka / MQTT / WebSockets (if needed)
- Deployment / DevOps: Docker, CI/CD pipelines
Planned enhancements:
- Multi-camera support & fusion
- Advanced anomaly detection (e.g., wrong direction, sudden stops)
- Integration with traffic signal control systems
- Mobile dashboard / notification support
- Optimization for edge devices (Raspberry Pi, Jetson, etc.)
- Scalability & clustering for large city deployment
Thank you for your interest in contributing! Here’s how you can help:
- Fork the repo
- Create a feature branch (
git checkout -b feature/X) - Implement and test your changes
- Submit a Pull Request with description
- Ensure code is properly documented, and tests added (if applicable)
Please follow the coding style and include clear commit messages.
MIT License
MIT License
...
- Maintainer: Thavva Jayasimha Reddy
- Email: 165299584+jayasimha-reddy-code@users.noreply.github.com
- GitHub: jayasimha-reddy-code
- Thanks to open-source projects and libraries used (OpenCV, TensorFlow, React, etc.)
- Inspirations and references from traffic‑analysis research papers and systems
This README aims to provide an overview, instructions, and guidance for developers and users interacting with SmartTrafficAnalyzer. Feel free to adapt and extend it as your project evolves.