Smart Parking

The Smart Parking application optimizes parking management with AI-driven video analytics. It identifies and counts available parking spaces in real-time, accurately detecting empty slots. By monitoring parking areas, it tracks occupancy changes and provides current parking availability information.

The solution records occupied parking spaces, enabling efficient use and reporting. It enhances parking efficiency and improves user experience by offering real-time insights into parking operations.

Using advanced technologies and pre-trained deep learning models, the application processes and analyzes video streams in real-time. Its modular architecture and integration capabilities allow users to customize and extend functionalities to meet specific needs.

Get Started

• 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
  • Run the Application
  • Access the Application and Components
    • Grafana UI
    • NodeRED UI
    • DL Streamer Pipeline Server
  • Stop the Application:
  • Other Deployment Option
  • Supporting Resources
• Release Notes
  • Version 1.2 - March 28th, 2025
    • High-Level Features

How to

• Customize Application
  • Overall System Architecture
  • DL Streamer Pipeline Server
    • Overview of DL Streamer Pipeline Server
    • Key DL Streamer Pipeline Server Components
      • Logging and General Configuration
      • Video Processing Pipelines
        • Object Detection Pipelines (YOLOv10 Series)
        • Object Tracking Pipelines
      • Configurable Parameters
      • Messaging Interface (MQTT)
    • DL Streamer Pipeline Server Workflow
  • Node-RED Flow for Data Processing
    • Overview of the Node-RED Flow
    • Key Node-RED Components
      • MQTT Input Nodes
      • Data Extraction Nodes
      • Euclidean and IoR Nodes (Loitering Detection)
      • Data Table Output Nodes
      • MQTT Output Nodes
    • Node-RED Workflow
  • Grafana Visualization
    • Overview of Grafana
    • Key Grafana Components
      • Data Sources
      • Dashboards and Panels
    • Setting up Grafana
      • Install and Launch Grafana
      • Add Your Data Source
      • Create Your Dashboard
    • Grafana Use Cases for Object Tracking and Loitering Detection
  • End-to-End Integration
  • Conclusion
• How to Deploy with Helm
  • Get Started
  • Prerequisites
  • Step 1: Download the Helm chart
  • Step 2: Configure and update the environment variables
  • Step 3: Deploy the application and Run multiple AI pipelines
  • Step 4: End the demonstration
  • Summary
  • Troubleshooting
    • Deploy with Intel GPU K8S Extension on Intel® Tiber™ Edge Platform
    • Deploying without Intel GPU K8S Extension
    • Error Logs

Source Code

• GitHub

Community and Support

• Get Help
  • Troubleshooting Steps
  • Support