The Cheating Surveillance System is designed to detect cheating during Online Interviews/Exams by monitoring head and pupil movements and identifying unauthorized mobile phone usage. This system integrates facial landmark detection with Shape Predictor 68 and object detection using YOLO, trained on a cellphone detection dataset from Roboflow.

• Head and Pupil Movement Detection: Uses dlib's Shape Predictor 68 to track facial landmarks and detect suspicious gaze patterns.
• Mobile Phone Detection: Utilizes a YOLOv12 model trained on the Roboflow Cellphone Detection Dataset to detect mobile phones in real-time.
• Real-Time Monitoring: Processes live video feeds for instant analysis and detection.
• Alert System: Detects and flags potential cheating behavior, such as excessive head or pupil movement in the left, right, up, or down direction for longer than the allowed time.

• Python
• OpenCV (for video processing)
• dlib (for facial landmark detection)
• YOLO (You Only Look Once) (for object detection)
• Roboflow Dataset (for training the mobile detection model)

cheating-surveillance/
│── models/                 # Contains trained YOLO weights and shape predictor model  
    |__ best_yolov8.pt
    |__ best_yolov12.pt
    |__ shape_predictor_68_face_landmarks.dat 
│── log                     # Screenshots
│── main.py                 # Entry point for real-time detection
│── requirements.txt        # Required dependencies
│── README.md               # Project documentation
│── head_pose.py            # Head movement detection
│── eye_movement.py         # Gaze Detection
│── mobile_detection.py     # Mobile detection
│── Demo_vid/               # Folder containing demo videos

Ensure you have the following installed:

• Python 3.8+
• OpenCV
• dlib
• torch (for YOLO)
• roboflow (for dataset access)

1. Clone the repository:

   git clone https://github.com/Sania-hasann/Cheating-Surveillance-System.git
   cd Cheating-Surveillance-System

2. Install dependencies:

   pip install -r requirements.txt

3. Download the Shape Predictor 68 model:

   wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
   bzip2 -d shape_predictor_68_face_landmarks.dat.bz2

4. Set up the YOLO model:
   • You have trained your YOLO model on the Roboflow Cellphone Dataset.
   • Download the trained YOLO weights and place the weights file in the models/ directory.

To start real-time monitoring, run:

python main.py

1. Facial Landmark Detection: Detects and tracks head movements and pupil direction.
2. YOLO-based Object Detection: Identifies mobile phones in the video feed.
3. Cheating Behavior Analysis: Flags abnormal behavior such as frequent head turning or gaze shifts.

• Gaze Detection
• Head Movement Detection
• Mobile Phone Detection

The mobile phone detection model is trained on the Roboflow Cellphone Detection Dataset. You can access it here: Roboflow Cellphone Dataset.

Feel free to submit issues and pull requests! If you have improvements or additional features, contribute by following these steps:

1. Fork the repository.
2. Create a new branch: git checkout -b feature-branch
3. Commit your changes: git commit -m "Add new feature"
4. Push to the branch: git push origin feature-branch
5. Open a Pull Request.

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

• dlib
• OpenCV
• YOLO
• Roboflow for dataset support