Automatic Invigilation Using Computer Vision

DOI

10.2991/ahis.k.210913.017How to use a DOI?

Keywords

Cheating Detection, Deep Learning, Object Detection, Smart Invigilation, YOLOv3

Abstract

Educational institutions determine students’ strengths and weaknesses through exams. Students find numerous ways to cheat in physical exams like exchanging their sheets, using hidden notes, getting good grades, fulfilling their parents’ expectations, and whatnot. Due to the physical limitations of human supervisors, typical invigilation methods cannot conduct successful exams while maintaining their integrity. An automated method based on computer vision to detect anomalous activities during exams is proposed in this study. This study centers around invigilating students’ suspicious behaviour during physical exams through closed-circuit television (CCTV) cameras. The proposed method uses You Only Look Once (YOLOv3) with residual networks as the backbone architecture to inspect cheating in exams. The obtained results show the credibility and efficiency of the proposed method. The experimental results are promising and demonstrate the invigilation of the students in the examination. In this work, achieve 88.03% accuracy for the detection of cheating in the classroom environment

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Manit Malhotra
AU  - Indu Chhabra
PY  - 2021
DA  - 2021/09/13
TI  - Automatic Invigilation Using Computer Vision
BT  - Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021)
PB  - Atlantis Press
SP  - 130
EP  - 136
SN  - 2589-4900
UR  - https://doi.org/10.2991/ahis.k.210913.017
DO  - 10.2991/ahis.k.210913.017
ID  - Malhotra2021
ER  -