EngageVision: AI-Powered Attention

Engagement Analysis with Head Pose Estimation

Engagement Analysis with Head Pose Estimation is a computer vision project that utilizes Mediapipe library for facial landmarks detection, OpenCV for computer vision tasks, and NumPy/Pandas for data manipulation. It estimates head pose and gaze direction to determine whether the user is engaged or not.

Features

• Head pose estimation.
• Gaze direction analysis.
• Engagement analysis based on head pose and gaze direction.
• Real-time visualization of facial landmarks and analysis results.
• Track the duration of continuous eye contact with the camera to measure attention span.
• Analyze variations in gaze direction to understand shifts in focus.

Parameters

Adjustable parameters are available in the script for customization:

• draw_gaze: Set to True to display gaze vectors.
• draw_full_axis: Set to True to display the full rotation axis.
• draw_headpose: Set to True to display head pose vectors.
• x_score_multiplier and y_score_multiplier: Multipliers to adjust the impact of gaze on head pose estimation.
• threshold: Threshold for averaging gaze scores between frames.

Installation

1. Clone the repository:

   git clone https://github.com/Manoj-2702/EyeTracking.git
   

2. Install all the required Dependencies: Requirements:

   • OpenCV
   • Mediapipe
   • Numpy
   • Pandas

   pip install -r requirements.txt
   

3. Usage:

   python eye_nose_detector.py
   

Results

The script provides real-time visual feedback on engagement and gaze direction. It also generates a summary of engagement statements and percentages after execution.

Sample Result

Engagement.Analysis.2023-11-12.20-22-11.mp4

License

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