Real Time intelligent Plate Tracking System(RTIPT)

Overview

This project implements an AI-powered system for real-time detection and recognition of vehicle number plates. It aims to streamline traffic management, enhance security, and provide efficient automated services such as toll collection and parking management.

Features

• Real-Time Detection: Detects and recognizes vehicle number plates in real-time from live video streams or static images.
• Accurate Recognition: Uses advanced OCR and machine learning models to accurately read number plates under various conditions.
• Automated Toll Collection: Integrates with toll management systems for seamless toll collection and payment processing.
• Parking Management: Facilitates automated entry and exit in parking facilities.
• Data Logging: Records and stores transaction details for reporting and analytics.
• Scalability: Designed to handle high volumes of data and scale with additional cameras and locations.

Tech Stack

• Frontend: React.js / Angular / Vue.js (for user interface)
• Backend: Node.js with Express or Python with Flask/Django
• Machine Learning: OpenCV, TensorFlow / Keras / PyTorch, YOLO, Tesseract OCR
• Database: MongoDB / MySQL / PostgreSQL
• DevOps: Docker, Kubernetes, AWS / Google Cloud / Azure
• Security: OAuth2 / JWT

Installation

1. Clone the Repository

   git clone https://github.com/khandolly786/RTI-PT.git
   cd Real time Intelligent plate tracking system

2. Install Dependencies

   • Frontend:

     cd frontend
     npm install

   • Backend:

     cd backend
     pip install -r requirements.txt

3. Configure Environment Variables

   Create a .env file in the root directory and add the required environment variables. Example:

   DATABASE_URL=your_database_url
   SECRET_KEY=your_secret_key

4. Run the Application

   • Frontend:

     cd frontend
     npm start

   • Backend:

     cd backend
     python app.py

Usage

1. Access the Web Interface

   • Open a web browser and navigate to http://localhost:3000 to access the user interface.

2. Upload Images / Stream Video

   • Use the interface to upload images or start a video stream for number plate detection and recognition.

3. View Results

   • The detected number plates and associated data will be displayed in real-time.

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Make your changes.
4. Commit your changes (git commit -am 'Add new feature').
5. Push to the branch (git push origin feature/your-feature).
6. Create a new Pull Request.

Contact

For any questions or support, please contact [email protected].

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