نظام تتبع وعدّ الأشخاص في الوقت الحقيقي
Real-Time People Tracking and Counting
This project uses YOLO and OpenCV to perform real-time people detection and tracking in video streams. Each detected person is assigned a unique tracking ID with visual bounding boxes, making it ideal for site monitoring, security, and movement analysis.
Project Overview
This system leverages the YOLO11 model for real-time people detection and tracking in video footage. It continuously detects individuals, assigns persistent tracking IDs across frames, and overlays bounding boxes and labels for accurate monitoring. Visualization is handled using OpenCV and CvZone for clear and structured output.
Key Features
YOLO11-Based People Detection
- 🧍 Accurate real-time detection of people using YOLO11.
- ⚡ High performance with optimized inference.
Persistent Object Tracking
- 🆔 Assigns unique track IDs to each detected person.
- 🔁 Maintains tracking consistency across frames.
Real-Time Visualization
- 📦 Displays bounding boxes, IDs, and class names.
- 🎨 Enhanced overlays using OpenCV and CvZone.
Optimized Processing
- ⏩ Frame skipping for efficient real-time performance.
- 🎥 Supports video files and can be adapted for webcam input.
Technology Stack
- 🐍 Programming Language: Python
- 🎥 Computer Vision: OpenCV
- 🧠 Object Detection & Tracking: YOLO11
- 🖥️ Visualization: CvZone
Use Cases
- 🏗️ Site Monitoring – Track workers and people in real time.
- 🔐 Security Surveillance – Detect and monitor unauthorized personnel.
- 📦 Equipment & Area Monitoring – Observe movement patterns.
- 🏙️ Smart Analysis – Adaptable for smart city and traffic analytics.
Conclusion
This project delivers a scalable and efficient real-time people tracking solution using YOLO11. With accurate detection, persistent tracking IDs, and clear visualization, it is well-suited for security, monitoring, and analytics applications. The system can be extended further for automation, alerts, and AI-driven decision-making.