This project is a surveillance system that utilizes face recognition technology to enhance security on a college campus. The system is designed to identify and track individuals by comparing their facial features with a pre-existing database of enrolled students faces. This README.md file provides an overview of the project and instructions for setting up and using the system.
The surveillance system offers the following features:
- Face detection: The system uses computer vision techniques to detect faces in real-time from video streams or recorded footage.
- Face recognition: By comparing the detected faces with a database of enrolled students, the system can recognize and identify individuals.
- Real-time tracking: A person can also be identified by a smartphone,and the system can track their movements in real-time, providing continuous surveillance.
- Database management: The system provides functionalities for managing the database of enrolled students, allowing administrators to add, remove, or update student faces and their ids.
Before setting up the surveillance system, ensure that the following prerequisites are met:
- Hardware requirements:
- Sufficient computing power to process video feeds in real-time(back end).
- One or more cameras capable of capturing video footage of the monitored areas or a smartphone is sufficient.
- Sufficient storage capacity to store the recorded student faces.
- Software requirements:
- Operating system supported by the face recognition library/framework.
- Face recognition library/framework installed (e.g., OpenCV, Dlib, TensorFlow).
- Database management system (e.g., MySQL, PostgreSQL) for storing student faces.
Follow these steps to install and set up the surveillance system:
- Clone or download the surveillance system repository from GitHub.
- Install the required dependencies by running the following command:
pip install -r requirements.txt
- Set up the database management system and create a database to store student faces.
- Prepare the face recognition model:
- If using a pre-trained model, download it and place it in the appropriate directory.
- If training a custom model, follow the instructions provided in the repository to train the model using the provided datasets.
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Configure the system parameters including camera settings, face recognition model path, database connection details, etc.
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Run the surveillance system by executing the main script:
python main.py -
The system will start processing feeds from the specified cameras and smartphone and send it to the backend which perform face detection and recognition, and generate appropriate details of the student.
Once the surveillance system is up and running, follow these guidelines to use it effectively:
- Add student records to the database: Use the provided database management interface to add student rolls and photographs.
- Monitor the system: Keep an eye on the surveillance system interface to observe real-time face detection, recognition, and tracking.
- Review identified individuals: Access the system's logs or user interface to review the list of identified individuals, along with their timestamps and locations.
- Manage the database: Use the provided interface to add, remove, or update student faces as necessary. Keep the database up to date to ensure accurate identification and tracking.
If you encounter any issues or errors while setting up or using the surveillance system, consider the following troubleshooting steps:
- Verify that all the required dependencies are installed correctly and up to date.
- Check the system configuration and ensure that all the necessary settings are correctly specified.
- Review the logs and error messages generated by the system to identify the source of the problem.
- Consult the project's documentation or seek assistance from the project community for further troubleshooting or support.
Contributions to the surveillance system project are welcome! If you would like to contribute, please follow these steps:
- Fork the repository on GitHub.
- Create a new branch with a descriptive name.
- Make the desired changes or improvements in your branch.
- Test the changes thoroughly to ensure they do not introduce any regressions.
- Submit a pull request, describing the changes made and providing any relevant information.
This project was inspired by the need for enhanced security on college campuses and the advancements in face recognition technology. We would like to acknowledge the following resources and libraries that contributed to the development of this system:
For any questions, feedback, or inquiries regarding the surveillance system, please contact:
- Name: Thilak Reddy
- Email: [email protected]
Feel free to reach out with any concerns or suggestions you may have. We appreciate your interest in the project!