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| Formato: | Recurso digital |
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Zenodo
2026
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| Acceso en línea: | https://doi.org/10.5281/zenodo.19236815 |
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| _version_ | 1866902036298072064 |
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| author | Mary Rufina Manu. R Loorth Kajol. N k. Pavithra DR. R Aroul Canessane |
| author_facet | Mary Rufina Manu. R Loorth Kajol. N k. Pavithra DR. R Aroul Canessane |
| contents | <p>Attendance management of schools in educational facilities is difficult because of the huge number, proxy attendance and absence of classes when a student reports to be present in school. The old manual orID-based systems are not effective, they can be easilycorrupted, and are not monitored in real-time, whichinhibits the free-will of the administrators to maintain accurate tracking of attendance and detect recurringabsenteeism. The current paper will suggest an AI-basedsurveillance system to cover the entire campus withYOLOv8 face detection and recognition. The cameras areplaced in classes and key areas on campuses and providelive video streams which are analyzed by the system toidentify the students automatically and capture theirattendance. The system makes a distinction between thosestudents who are on campuses and those who are inclasses, which create centralized records of attendance andauto-notifications to authorities in case of discrepancies.The system employs the deep learning technique,automated reporting, and real-time monitoring tominimize proxy attendance, increase accountability, anddeliver actionable analytics about student behavior. Thisexperience shows how AI can be used to revolutionizeclassroom management and enhance a disciplined andactive learning experience.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19236815 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Campus-Wide AI Surveillance System for Accurate Attendance and Identifying Class Skippers Mary Rufina Manu. R Loorth Kajol. N k. Pavithra DR. R Aroul Canessane <p>Attendance management of schools in educational facilities is difficult because of the huge number, proxy attendance and absence of classes when a student reports to be present in school. The old manual orID-based systems are not effective, they can be easilycorrupted, and are not monitored in real-time, whichinhibits the free-will of the administrators to maintain accurate tracking of attendance and detect recurringabsenteeism. The current paper will suggest an AI-basedsurveillance system to cover the entire campus withYOLOv8 face detection and recognition. The cameras areplaced in classes and key areas on campuses and providelive video streams which are analyzed by the system toidentify the students automatically and capture theirattendance. The system makes a distinction between thosestudents who are on campuses and those who are inclasses, which create centralized records of attendance andauto-notifications to authorities in case of discrepancies.The system employs the deep learning technique,automated reporting, and real-time monitoring tominimize proxy attendance, increase accountability, anddeliver actionable analytics about student behavior. Thisexperience shows how AI can be used to revolutionizeclassroom management and enhance a disciplined andactive learning experience.</p> |
| title | Campus-Wide AI Surveillance System for Accurate Attendance and Identifying Class Skippers |
| url | https://doi.org/10.5281/zenodo.19236815 |