AI-Powered Exam Integrity Guardian: Real-Time Classroom Cheating Detection System Using YOLOv8 and Pose Estimation
Real-time exam cheating detection system built with Django 5.0 and YOLOv8 pose estimation — flags phone usage, suspicious posture, and paper passing via webcam or uploaded video, with instant email alerts to invigilators.
Technology Used
Django 5.0 | Python 3.8+ | YOLOv8 Object Detection | YOLOv8-Pose Estimation | Ultralytics | OpenCV | Computer Vision | NumPy | Pillow | HTML5 | CSS3 Glassmorphism | JavaScript | Chart.js | Three.js | SQLite | Email SMTP | Real-time Video Processing
What Is This Project?
AI-Powered Exam Integrity Guardian is a computer vision system that watches examination halls in real time and flags cheating behavior automatically. It uses two separate YOLOv8 models — one for object detection, one for pose estimation — to catch phone usage, suspicious body posture, and hand-to-hand paper passing between students. Instead of relying on a handful of human invigilators watching dozens of students, the system runs continuous frame-by-frame analysis and logs every incident with a timestamp and confidence score.
Key Features
- YOLOv8 object detection trained specifically to spot mobile phones at varying angles and lighting conditions
- YOLOv8-Pose tracking of 17 body keypoints to detect head turning, unusual bending, and abnormal arm positions
- Hand-proximity calculation between adjacent students to catch document passing in progress
- Seat-position mapping that flags when a student leaves their assigned spot or approaches another desk
- Live webcam monitoring mode with bounding boxes and confidence scores overlaid on the video feed
- Batch video upload with frame-by-frame processing for reviewing recorded exam sessions after the fact
- Configurable email alert system with per-room recipient lists and adjustable confidence thresholds
- Glassmorphism dashboard built with Three.js backgrounds and Chart.js charts for detection trends
How It Works
Video frames from a webcam or uploaded file are passed through the YOLOv8 detection model and the YOLOv8-Pose model in parallel. Detected phones, hands, and pose keypoints are analyzed against threshold rules to classify behavior as normal or suspicious. Flagged incidents are saved to the database with a screenshot, confidence score, and timestamp, and an SMTP email alert is fired to the configured invigilator list. All detections then appear on the Django-powered dashboard for review and reporting.
Tech Stack
- Django 5.0 — handles the backend logic, database models, and admin views with a clean MVC structure
- Ultralytics YOLOv8 — chosen for its speed and accuracy on both object detection and pose estimation without needing two separate frameworks
- OpenCV — manages video capture, frame extraction, and image preprocessing before frames hit the model
- NumPy and Pillow — handle array operations and image manipulation for annotation and cropping
- Chart.js and Three.js — power the dashboard's trend charts and animated glassmorphism background
- SQLite — lightweight storage suited for a project of this scale, easy to swap for PostgreSQL in production
- SMTP email — delivers real-time alerts without needing a third-party notification service
Who Is This For?
Computer science and IT students looking for a final year project with real computer vision depth rather than a toy classifier. Also useful for developers building proctoring tools for online exam platforms, and college administrators exploring automated invigilation for large exam halls.
What's Included
- Complete Django source code with comments
- Pre-trained YOLOv8 and YOLOv8-Pose model weights
- Setup guide and installation documentation
- Sample test videos for demonstration
- Database schema and migration files
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