نظام ذكي لمراقبة عمليات طاقم الخدمات الأرضية بالمطارات

AI-Powered Airport Ground Crew Operations Monitoring System

AI-powered Airport Ground Crew Operations Monitoring system that utilizes computer vision and Google Gemini AI to analyze real-time video footage, detect ground crew activities, and generate structured reports for safety compliance, efficiency optimization, and operational insights in airport logistics.

Project Overview

This project enhances airport logistics, safety monitoring, and operational efficiency using computer vision, artificial intelligence, and automation. By leveraging Google Gemini AI and OpenAI’s API, the system captures real-time video footage from airport tarmacs, detects ground crew activities, and generates structured operational reports.

Key Features & Components

1. Real-Time Video Processing

• 🎥 Captures live webcam or pre-recorded airport video footage.
• 🖼️ Continuously processes frames to detect ground crew activities.
• 🛠️ Uses OpenCV for video capture, resizing, and annotation.

2. Region of Interest (ROI) Detection

• 📐 Uses polygon-based ROI to isolate critical operational zones.
• 🎯 Reduces noise and improves AI inference accuracy.
• 🖍️ Highlights ROI visually using colored polylines.

3. AI-Powered Activity Recognition (Google Gemini AI)

• 🧠 Extracts Base64-encoded images from video frames.
• 📡 Sends cropped images to Google Gemini AI via OpenAI API.
• 📊 Receives structured activity insights in table format.
• ✈️ Detects activities such as refueling, baggage handling, pushback operations, and crew coordination.

4. Asynchronous Processing

• ⚙️ Uses Python threading to prevent video processing delays.
• ⏱️ Sends frames to AI every 5 seconds to reduce API load.
• 🚀 Ensures smooth and uninterrupted monitoring.

5. Automated Report Generation

• 📝 Saves AI-generated insights into structured text files.
• 📁 File naming format: Airport_Ground_Crew_Operations_{Area}.txt
• 📚 Creates historical records for audits and compliance.

Technical Workflow

1. Crop ROI from video frame using polygon detection.
2. Convert cropped image to Base64 format.
3. Send image to Google Gemini AI via OpenAI API.
4. Receive structured activity descriptions.
5. Save reports and display annotated video feed.

Applications & Use Cases

• 🛡️ Airport safety and compliance monitoring.
• 📊 Operational efficiency and workflow optimization.
• 🧾 Automated documentation and auditing.
• 🧠 AI-driven decision support for ground handling.

Technologies Used

• 🐍 Python
• 🎥 OpenCV
• 🧠 Google Gemini AI
• 🤖 OpenAI API (via OpenRouter)
• 🧵 Threading
• 🔢 NumPy
• 🔐 Base64 Encoding

Future Enhancements

• ✅ Multi-area airport monitoring.
• 📡 RTSP / IP camera live streaming.
• 🧠 YOLOv8 integration for object detection.
• 🔔 Real-time safety alerts.
• 📊 Interactive dashboard visualization.

Conclusion

This AI-powered Airport Ground Crew Operations Monitoring system delivers real-time insights, automated reporting, and enhanced safety compliance. By combining computer vision, Google Gemini AI, and OpenAI’s API, it provides airports with a powerful tool to optimize ground operations and ensure regulatory compliance.