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●15 reads●MIT License

Parking-Space-Counter-Directing_Project

Table of contents

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

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.

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.

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)

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

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.

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.

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.

Resources

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.

Models

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