Smart Parking

The Smart Parking application uses AI-driven video analytics to optimize parking management. It identifies and counts available parking spaces in real-time, ensuring accurate detection of empty slots. By continuously monitoring parking areas, it tracks changes in occupancy and provides up-to-date information on parking availability.

This solution also records the status of occupied parking spaces, enabling efficient utilization and reporting. Designed to enhance parking efficiency, the application improves user experience by delivering real-time insights into parking operations.

By leveraging cutting-edge technologies and pre-trained deep learning models, this application enables real-time processing and analysis of video streams, making it an ideal solution. Built on a modular architecture and integration capabilities ensure that users can easily customize and extend its functionalities to meet their specific needs.

Get Started

• Smart Parking
  • Overview
    • Key Features
  • How It Works
    • Components
  • Learn More
• System Requirements
  • Supported Platforms
  • Minimum Requirements
  • Software Requirements
  • Compatibility Notes
  • Validation
• Get Started
  • Prerequisites
  • Set up and First Use
  • Stop the Containers
  • Next Steps
  • Troubleshooting
  • Supporting Resources
• Release Notes
  • Version 1.2 - March 28th, 2025
    • High-Level Features

How to

• Customize Application
  • 1. Introduction
  • 2. Overall System Architecture
  • 3. EVAM Edge Video Analytics Microservice
    • 3.1. Overview of EVAM
    • 3.2. Key EVAM Components
      • 3.2.1. Logging and General Configuration
      • 3.2.2. Video Processing Pipelines
        • 3.2.2.1. Object Detection Pipelines (YOLOv10 Series)
        • 3.2.2.2. Object Tracking Pipelines
      • 3.2.3. Configurable Parameters
      • 3.2.4. Messaging Interface (MQTT)
    • 3.3. EVAM Workflow
  • 4. Node-RED Flow for Data Processing
    • 4.1. Overview of the Node-RED Flow
    • 4.2. Key Node-RED Components
      • 4.2.1. MQTT Input Nodes
      • 4.2.2. Data Extraction Nodes
      • 4.2.3. Euclidean and IoR Nodes (Loitering Detection)
      • 4.2.4. Data Table Output Nodes
      • 4.2.5. MQTT Output Nodes
    • 4.3. Node-RED Workflow
  • 5. Grafana Visualization
    • 5.1. Overview of Grafana
    • 5.2. Key Grafana Components
      • 5.2.1. Data Sources
      • 5.2.2. Dashboards and Panels
    • 5.3. Setting up Grafana
      • 5.3.1. Install and Launch Grafana
      • 5.3.2. Add Your Data Source
      • 5.3.3. Create Your Dashboard
    • 5.4. Grafana Use Cases for Smart Parking
  • 6. End-to-End Integration
  • 7. Conclusion

Source Code

• GitHub

Community and Support

• Get Help
  • Support