The Student Surveillance project is an automated surveillance system that utilizes advanced technologies to monitor and identify students using a video stream from a camera. The system employs facial recognition to match individuals against a database of students. Upon identification, relevant parties are notified via a Telegram alert, providing details of the recognized students along with the timestamp.
- DeepFace Library: Used for facial recognition.
- Dlib: Utilized for face detection.
- Facenet: Employed to obtain facial embeddings for recognition.
- MongoDB: Storage of student details.
- Flask: Web framework for creating the user interface.
- HTML and CSS: Used to design and style the web interface.
- Multithreading: Implemented for efficient real-time detection.
- Real-time facial recognition using a video stream.
- Telegram alerts for identified students with timestamp details.
- Database storage for student records.
- Web interface for managing student details.
- Multithreading for efficient real-time detection.
- Ensure you have Python installed.
- Install required libraries using
requirements.txt.
- Clone the repository:
git clone https://github.com/your-username/student-surveillance.git - Navigate to the project directory:
cd student-surveillance - Install dependencies:
pip install -r requirements.txt
Set up your environment variables by following these steps:
- Create a copy of the
.example.envfile and name it.env. - Open the newly created
.envfile and replace the placeholder values with your own.
- To start surveillance:
python main.py - To start the web interface:
python app.py - Access the web interface at
http://localhost:5000to manage student records.