Smart Parking System with Computer Vision

Table of Contents

1. Introduction
2. Features
3. System Requirements
4. Installation Guide
5. How It Works
6. Real-world Implementation
7. Future Scope
8. Resources
9. Conclusion

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Introduction

The Smart Parking System leverages Computer Vision to automatically detect and manage parking spaces in real-time. This solution is ideal for high-traffic areas like parking lots, shopping malls, airports, and urban environments, where efficient parking management is critical for reducing congestion and enhancing user satisfaction.

Key highlights include:

• Real-time video feed processing to determine parking space occupancy.
• Visual indicators for available and occupied spots.
• Direction guidance to available spaces for users.

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Features

• Parking Space Detection: Identifies if a parking spot is empty or occupied.
• Real-time Status: Displays up-to-date information on parking space availability.
• Dynamic Direction Arrows: Guides users to the nearest available parking space.
• Real-time Notifications: Sends updates via SMS or Bluetooth.
• Color-coded Indicators: Marks empty spots in green and occupied spots in red.
• Spot Labels: Labels each parking spot (e.g., A1, A2, B1) for easy reference.

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System Requirements

Hardware

• Camera: High-definition video capture for parking lot monitoring.
• Computer: At least 8 GB RAM and a modern CPU/GPU for real-time processing.

Software

• Python 3.x
• Required Libraries:
  • OpenCV (Computer Vision Processing)
  • cvzone (Integration with OpenCV)
  • NumPy (Numerical Operations)
  • Pickle (Data Storage for Spot Positions)
  • Twilio/Nexmo (SMS Notifications)

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Installation Guide

1. Clone the Repository

   git clone https://github.com/bashitalishaikh/smart-parking-system.git
   cd smart-parking-system

2. Install Dependencies
   Ensure Python 3.x is installed, then run:

   pip install opencv-python cvzone numpy pickle twilio

3. Set Up Parking Lot Data

   • Use the CarParkPos file to define parking spot coordinates.
   • Example:

     [(100, 150), (200, 150), (300, 150)]

4. Run the Parking System
   Start the system with:

   python main.py

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How It Works

1. Input Video Feed: A camera captures the parking lot view.
2. Image Processing:
   • Techniques like Gaussian Blur, Thresholding, and Edge Detection are applied.
3. Spot Detection:
   • Each parking spot is analyzed to determine its status.
4. Spot Indicators:
   • Free spots are marked green; occupied spots are marked red.
   • Directions to free spots are displayed dynamically.
5. Real-time Updates:
   • The system continuously monitors and updates the status.

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Real-world Implementation

Applications

1. Urban Parking: Helps reduce search time for parking in crowded areas.
2. Malls & Airports: Ideal for managing parking in high-traffic public spaces.
3. Corporate Parking: Efficiently manages employee and visitor parking.

IoT Integration

• Sensors: IoT devices like motion or pressure sensors enhance detection accuracy.
• Mobile App: Real-time availability updates and reservation features.

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Future Scope

1. Automated Entry & Exit:
   • Ticketless system with vehicle detection and automatic spot status updates.
2. Real-time Reservations:
   • Users can pre-book parking spots through a mobile or web interface.
3. SMS & Notifications:
   • Instant updates about available or reserved parking spots.
4. Bluetooth Integration:
   • Guides users to free spots via Bluetooth notifications.

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Resources

• OpenCV Documentation
• TensorFlow
• PyTorch
• Twilio for SMS
• IoT Resources:
  • Arduino
  • Raspberry Pi

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Conclusion

The Smart Parking System is a transformative solution for managing parking efficiently. Its integration with real-time monitoring, mobile apps, and IoT devices offers a seamless and user-friendly experience. Future enhancements like automated entry/exit, reservation systems, and advanced notifications can further revolutionize parking management, making it indispensable for urban development.

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