Automated Invigilation System for Detection of Suspicious Activities during Examination

Adil,; Simon, Rajbala; Khatri, Sunil Kumar

doi:10.1109/aicai.2019.8701263

Cited by 12 publications

(2 citation statements)

References 18 publications

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“…Haar-like features are commonly known for detecting a human face in object recognition. In [2], Adil et al detects different suspicious activities in the exam. On the basis of a certain threshold, the model recognizes activities like hands in contact, sneaking the fellow student's paper.…”

Section: Related Workmentioning

confidence: 99%

“…There are psychological and social reasons why students opt to cheat in exams, such as parent's pressure, feeling of incompetency, want better grades, time constraints, fear of failure, and take a risk with less fear of being caught [1]. The research of Dr. Donald McCabe's and the International Centre for Academic Integrity survey and analysis states that about 68% of undergrads admit to using unfair means or cheating during exams [2]. There are various means of cheating that students adopt in exams.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Invigilation Using Computer Vision

Malhotra¹,

Chhabra²

2021

Atlantis Highlights in Computer Sciences

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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

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%