Automated Cheating Detection based on Video Surveillance in the Examination Classes
Keywords:Surveillance videos, videos analysis, behaviors recognition, anomaly detection, neural networks.
A major issue in the field of education is exam malpractice and other forms of academic dishonesty, where efforts made towards the assessment of students are undercut. In order to address this menace, a cheating detection system has been proposed in this study. The system can detect cheating in real-time. The proposed system uses video surveillance to monitor the activities of the student during exams, especially abnormal behavior. The development of the system involved the use of three different techniques, with different functions. The first technique helps in determining the direction of the students’ heads when they move from their initial direction, which is the exam script. Some form of cheating involves students peeping at the exam scripts of other people writing the same exam. Whenever the system observes a deviation that exceeds the set threshold, it classifies the behavior as abnormal. The second technique works with the movement of the student’s iris. It detects when a student’s iris moves in a different direction to copy answers from written documents like mobile phones, books, hands, summary papers. The third technique is used in identifying the contact between a student’s hands and face, as well as that between different students for shared abnormal behavior detection between students, such as sharing of incriminating materials. When any of these is detected, an automatic alarm alerts the authority about the abnormal behavior that has been detected, thereby minimizing the error rate that can occur as a result of manual monitoring.
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Copyright (c) 2022 Haider Th.Salim Alrikabi, Roa'a M. Al_airaji , Ibtisam A. Aljazaery, Abdul Hadi M. Alaidi
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