Person Detection System with PyQt and YOLO
Project Overview
A robust desktop application built with PyQt6 that leverages YOLO object detection to identify and track people in video streams. This project combines computer vision capabilities with a user-friendly interface for real-time person detection and works both offline and online. The system features modular architecture for easy maintenance and extension, multiple camera support with automatic detection, and Telegram bot integration for remote monitoring.
Key Features
- Real-time person detection using YOLOv8
- Modular architecture for easy maintenance and extension
- Multiple camera support with automatic detection
- Telegram bot integration for remote monitoring
- Configurable accuracy levels (0.25, 0.5, 0.75)
- Face detection within person bounding boxes
- Database management for users and settings
- Professional package structure following Python best practices
Technology Stack
PyQt6
Modern GUI framework for the desktop application interface
YOLO v8
State-of-the-art object detection model for real-time person detection
OpenCV
Computer vision library for image processing and camera management
Results & Impact
The application successfully demonstrates real-time person detection with high accuracy and performance. It has been tested on Unix-based systems and provides reliable monitoring capabilities for security and surveillance applications. The modular design allows for easy integration with other systems and future enhancements.
Future Enhancements
- Add facial recognition capability to identify unique individuals
- Generate detailed statistics and reports for each identified person
- Full support for Windows operating systems
- Upgrade to newer YOLO versions for improved accuracy
- Docker containerization support
- REST API for integration with other systems
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