A real-time movement detection application built with C# and OpenCvSharp that provides comprehensive motion monitoring and automatic video recording capabilities.

• Real-time Motion Detection: Advanced computer vision algorithms using background subtraction
• Dual Video Display: Live feed and motion detection overlay side by side
• Auto-Recording: Automatically records video when motion is detected
• Manual Recording: Start/stop recording on demand
• Configurable Sensitivity: Real-time adjustment from 1-100
• Background Learning: Automatically adapts to lighting and scene changes

• Professional GUI: Clean Windows Forms interface
• Real-time Controls: Start/Stop detection, sensitivity adjustment, background reset
• Status Monitoring: Live motion detection status and event logging
• Recording Controls: Manual recording toggle and auto-record checkbox

• Event Logging: Comprehensive logging with timestamps
• Configuration System: JSON-based settings management
• Error Handling: Robust camera and processing error management
• Extensible Design: Modular architecture for easy feature additions

• OS: Windows 10/11
• Framework: .NET 8.0 or higher
• Memory: 2GB RAM minimum
• Camera: DirectShow compatible webcam
• Storage: 100MB for application + space for recordings

1. Clone the repository:

   git clone <repository-url>
   cd movement-detection

2. Run the application:

   # Option 1: Use the demo script
   .\demo.ps1
   
   # Option 2: Use the batch file
   .\run.bat
   
   # Option 3: Direct command
   dotnet run --project MovementDetection.csproj

# Restore dependencies
dotnet restore

# Build the project
dotnet build

# Run the application
dotnet run

1. Start the Application - Camera initializes automatically
2. Click "Start Detection" - Begin motion monitoring
3. Adjust Sensitivity - Use the slider (recommended: 25-50)
4. Enable Auto-Record - Check the "Auto Record" box for automatic recording
5. Monitor Status - Watch real-time updates in the status panel

• Red rectangles highlight detected motion areas
• Green contours show exact movement boundaries
• Status updates appear in real-time
• Event logging tracks all motion events with timestamps

• Auto-Recording: Starts when motion detected, stops 5 seconds after motion ends
• Manual Recording: Click "Start Recording" for continuous recording
• File Management: Videos saved to Recordings/ with timestamp filenames
• Event Logs: All activities logged to Logs/ directory

Edit config.json to customize behavior:

{
  "Motion": {
    "Threshold": 25.0,           // Detection sensitivity
    "MinContourArea": 500.0,     // Minimum motion area
    "LearningRate": 0.01,        // Background learning rate
    "LearningFrames": 30         // Frames to learn background
  },
  "Camera": {
    "DeviceIndex": 0,            // Camera device number
    "FrameWidth": 640,           // Video resolution width
    "FrameHeight": 480,          // Video resolution height
    "Fps": 30.0                  // Frame rate
  },
  "Recording": {
    "AutoRecord": true,          // Enable auto-recording
    "OutputDirectory": "Recordings",
    "VideoFormat": "avi",
    "VideoCodec": "MJPG"
  }
}

├── Source Files
│   ├── MainForm.cs              # Main application window and UI logic
│   ├── MotionDetector.cs        # Core motion detection algorithms
│   ├── VideoRecorder.cs         # Video recording functionality
│   ├── MotionEventLogger.cs     # Event logging system
│   ├── AppConfig.cs             # Configuration management
│   └── Program.cs               # Application entry point
├── Configuration
│   ├── MovementDetection.csproj # Project file with dependencies
│   ├── config.json              # Runtime configuration
│   └── .gitignore               # Git ignore rules
├── Documentation
│   ├── README.md                # This file
│   ├── USER_GUIDE.md            # Detailed user manual
│   └── PROJECT_SUMMARY.md       # Development summary
├── Scripts
│   ├── demo.ps1                 # PowerShell demo script
│   └── run.bat                  # Simple startup script
└── Output Directories
    ├── Recordings/              # Recorded video files
    └── Logs/                    # Event and system logs

1. Frame Capture: 30 FPS video processing
2. Preprocessing: Grayscale conversion and Gaussian blur
3. Background Modeling: Adaptive background subtraction
4. Motion Detection: Frame differencing with binary thresholding
5. Noise Reduction: Morphological operations (opening/closing)
6. Contour Analysis: Area-based motion filtering
7. Visual Feedback: Bounding boxes and contour overlay

• OpenCvSharp4: Computer vision library for .NET
• OpenCvSharp4.runtime.win: Windows runtime for OpenCV
• OpenCvSharp4.Extensions: Additional OpenCV extensions
• System.Text.Json: JSON configuration management

• Home security monitoring
• Office/warehouse surveillance
• Perimeter intrusion detection
• Automated security recording

• Motion-activated lighting systems
• Automated recording for events
• Pet/wildlife monitoring
• Visitor detection and logging

• Computer vision algorithm testing
• Motion analysis research
• Educational computer vision demonstrations
• Prototype surveillance systems

"Could not open camera!" error

• Ensure webcam is connected and recognized by Windows
• Close other applications using the camera (Teams, Skype, etc.)
• Try different camera device index in config.json

Poor motion detection accuracy

• Adjust sensitivity slider (try values 20-50)
• Reset background when lighting changes
• Ensure stable camera mounting (reduce vibrations)
• Check for adequate lighting conditions

Performance issues

• Close unnecessary applications
• Reduce video resolution in config.json
• Check available system memory

• Multiple camera support
• Email/SMS notifications
• Web-based remote monitoring
• Mobile app companion
• Object tracking and classification
• Face recognition integration

• Machine learning-based detection
• Cloud storage integration
• Motion pattern analysis
• Automated alert systems
• Integration with home automation platforms

Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests.

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes and test thoroughly
4. Submit a pull request with detailed description

This project is open source and available under the MIT License.

• OpenCvSharp: Excellent .NET wrapper for OpenCV
• OpenCV Community: Amazing computer vision library
• Microsoft: .NET framework and development tools

Built with ❤️ using C# and OpenCV