The Automated Video Meeting Analysis project aims to develop a real-time system for analyzing video meetings using machine learning. The system focuses on detecting anomalies, analyzing emotions and behaviors, and generating insightful reports. It leverages open-source tools and technologies to provide an efficient solution for monitoring and improving meeting interactions.
- Programming Language: Python
- Video Processing: OpenCV
- Face Detection and Recognition: Dlib (pre-trained models)
- Emotion Detection: Custom machine learning models (multilingual support)
- Web Framework: Flask
- Database: SQLite
- Development Environment: Google Colab
- Version Control: GitHub
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Real-time Video Processing
- Captures and processes video streams in real-time.
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Face Detection and Recognition
- Utilizes pre-trained models to detect and recognize faces.
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Emotion Detection
- Analyzes facial expressions to detect emotions with multilingual support.
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Feature Extraction and Clustering
- Extracts relevant features from video frames and performs clustering.
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Statistical and Graphical Analysis
- Analyzes data statistically and visualizes results using graphs.
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Automated Reporting and Dashboard
- Generates reports and provides a dashboard for real-time analytics and alerts.
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Clone the Repository
git clone https://github.com/baranidharan27/video-meeting-analysis.git cd video-meeting-analysis
2 . Install Dependencies Ensure you have Python 3.x installed. Install the required libraries using:
pip install -r requirements.txt- Start the application using:
python src/app.py- Testing Unit tests are located in the test directory. Run the tests using:
python -m unittest discover -s tests- Contributing If you'd like to contribute to this project, please follow these steps:
-Fork the repository.
-Create a feature branch (git checkout -b feature-branch).
-Commit your changes (git commit -am 'Add new feature').
-Push to the branch (git push origin feature-branch).
-Create a new Pull Request.