Spaces:
Runtime error
Runtime error
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <link rel="stylesheet" href="report.css"> | |
| <link rel="stylesheet" href="common.css"> | |
| <title>Attendance System Report</title> | |
| </head> | |
| <body> | |
| <nav> | |
| <img src="https://www.gsfcuni.edu.in/public/logo/White1.png" alt="Logo"> | |
| <ul> | |
| <li><a href="/">Image</a></li> | |
| <li><a href="/camera">Camera</a></li> | |
| <li><a href="/table">Table</a></li> | |
| <li><a href="/report">Report</a></li> | |
| </ul> | |
| </nav> | |
| <main class="main"> | |
| <section class="container"> | |
| <h1>Attendance System Project</h1> | |
| <h2>Introduction</h2> | |
| <p> | |
| The Attendance System Project is an innovative solution developed as part of the Computer Vision | |
| subject. It leverages the power of YOLO (You Only Look Once), a state-of-the-art object detection | |
| framework, to automate attendance tracking in various contexts, such as classrooms, meetings, or events. | |
| </p> | |
| <h2>Features</h2> | |
| <ul> | |
| <li>Real-time Object Detection</li> | |
| <li>Face Recognition</li> | |
| <li>Automatic Attendance Recording</li> | |
| <li>User-friendly Web Interface</li> | |
| </ul> | |
| <h2>Implementation</h2> | |
| <p> | |
| The project was implemented using Python and the YOLO framework. It involves training the YOLO model to | |
| recognize faces and track them in real-time using a camera feed. When a recognized face is detected, the | |
| system records the attendance of the corresponding individual. | |
| </p> | |
| <h2>Challenges</h2> | |
| <p> | |
| While implementing the project, several challenges were encountered, including real-time processing, | |
| optimizing the YOLO model for speed, and ensuring accurate face recognition under various lighting | |
| conditions. | |
| </p> | |
| <h2>Conclusion</h2> | |
| <p> | |
| The Attendance System Project demonstrates the potential of computer vision and deep learning in | |
| automating attendance tracking processes. It offers a robust and efficient solution for educational | |
| institutions and organizations seeking to streamline attendance management. | |
| </p> | |
| </section> | |
| </main> | |
| <footer> | |
| Developed by Kshipra and Viraj | |
| </footer> | |
| </body> | |
| </html> |