HIDS is a locally deployed Home Intrusion Detection System designed to enhance home security through real-time monitoring and alerting. Leveraging cutting-edge technologies like YOLO (You Only Look Once) object detection and Flask web framework, this system detects intruders and provides immediate notifications to homeowners via email.

• Real-time Object Detection:

  • Utilizes YOLO object detection model to identify intruders in real-time from webcam footage.
  • Utilizes YOLO (You Only Look Once) object detection model, a state-of-the-art deep learning framework.
    • YOLO employs a single neural network to simultaneously predict bounding boxes and class probabilities.
    • It divides the image into a grid and predicts bounding boxes and probabilities for each grid cell, enabling efficient detection.
    • YOLOv3, the version implemented in HIDS, enhances accuracy and speed by integrating multiple detection scales and advanced feature extraction techniques.

• Email Alerting:

  • Sends instant email notifications to homeowners upon detecting suspicious activity, enabling rapid response.
    • Utilizes Simple Mail Transfer Protocol (SMTP) for sending emails securely over the internet.
    • Implements two-factor authentication (2FA) architecture of Google Workspace for enhanced email security.
      • Google Workspace 2FA adds an extra layer of protection by requiring users to verify their identity through a secondary method, such as a mobile device or authenticator app.
      • This ensures that only authorized users can access the email alerting system, preventing unauthorized access and enhancing overall system security.

• Flask Web Interface:

  • Offers a user-friendly web interface powered by Flask for easy monitoring of detected intrusions and system status.
    • Utilizes the lightweight and flexible architecture of Flask, a micro web framework for Python, to create dynamic web applications.
    • Flask follows a modular design, allowing developers to build scalable and maintainable web interfaces by organizing code into smaller, reusable components.
      • This modular approach enhances performance by reducing overhead and improving code maintainability, resulting in faster response times and better overall user experience.

1. Clone the HIDS repository to your local machine:

   git clone <repository-url>

2. Install required dependencies:

   pip install -r requirements.txt

3. Configure email settings in config.py to enable email alerting.

4. Run the application:

   python app.py

5. Access the web interface at http://localhost:5000 to monitor intrusions and system status.

• Launch the HIDS system by running app.py.
• Navigate to the web interface to view live camera feed and intrusion alerts.
• Receive email notifications upon detecting intruders for immediate action.

Contributors to HIDS are valued and appreciated! Special thanks to all those who have contributed to this project:

1. David Grace - Tech Lead & Project Manager
2. Adersh S Thomas - Developer (Python)
3. Akshay S P - Developer (UI/UX)
4. Jayasurya - Intern

• Initial release featuring basic YOLO person-class detection with Alarm Trigger functionality.
• Capable of distinguishing humans from other animate and inanimate objects.
• Implemented real-time detection and alerting system.

• Introduced Flask Dashboard Interface.
• Provided clients with initial intruder detection alerts and administrative control.
• Enhanced user experience with interactive controls and customizable settings.

• Integrated SMTP email alerting system.
• Delivered notifications to client email addresses with precise timestamps.
• Implemented classification of humanoids as daytime humanoids or intruders based on predefined curfew times.
• Enhanced alerting system with customizable email templates and recipient lists.

• Enhanced overall system performance and responsiveness.
• Revamped user interface with a cyberpunk-themed design.
• Provided a visually appealing and immersive environment for monitoring and control.
• Improved system stability and reliability through optimized code architecture.
• Streamlined user workflows with intuitive navigation and enhanced usability features.

• The term "BleedingEdge" denotes the primary development branch.
• It houses the latest updates and experimental features.
• New updates and features are developed as deviations from the BleedingEdge branch, ensuring a continuous cycle of innovation and improvement.

This software is provided as-is and without warranty. Use at your own risk.

For any inquiries or support, please contact maintainer-email.

This project is licensed under the MIT License - see the LICENSE file for details.