WatchGuard is a high-performance web application designed to enhance security surveillance capabilities. This project leverages advanced image processing algorithms to identify weapons, trespassing, fire, and smoke in surveillance footage, providing real-time alerts and precise location information for immediate action.
- Advanced Image Processing: Utilizes machine learning algorithms to detect and classify security threats in real-time, including weapons, trespassing, fire, and smoke.
- Real-time Alerts: Sends instant notifications with precise location information, enabling swift response to security breaches.
- High-Performance Dashboard: Integrates multiple models with real-time status updates and caching, ensuring efficient performance and scalability.
- Incident Detection Accuracy: Boosts incident detection accuracy by 40%, ensuring reliable threat identification.
- Response Time Optimization: Slashes response time by 50%, enabling rapid response to security incidents.
- Frontend: Built using React JS for a seamless user experience.
- Backend: Powered by Django for robust and scalable server-side logic.
- Database: Utilizes MySQL for efficient data storage and retrieval.
- Clone the repository: git clone https://github.com/AzeemIdrisi/WatchGuard.git
- Install dependencies: pip install -r requirements.txt
- Set up the database: python manage.py migrate
- Run the application: python manage.py runserver
Contributions are welcome! If you'd like to contribute to WatchGuard, please fork the repository, make your changes, and submit a pull request.
WatchGuard is licensed under the MIT License. See LICENSE for details.