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Examinations are a crucial part of the learning process, and academicinstitutions invest significant resources into maintaining their integrity bypreventing cheating from students or facilitators. However, cheating has becomerampant in examination setups, compromising their integrity. The traditionalmethod of relying on invigilators to monitor every student is impractical andineffective. To address this issue, there is a need to continuously record examsessions to monitor students for suspicious activities. However, theserecordings are often too lengthy for invigilators to analyze effectively, andfatigue may cause them to miss significant details. To widen the coverage,invigilators could use fixed overhead or wearable cameras. This paperintroduces a framework that uses automation to analyze videos and detectsuspicious activities during examinations efficiently and effectively. Weutilized the OpenPose framework and Convolutional Neural Network (CNN) toidentify students exchanging objects during exams. This detection system isvital in preventing cheating and promoting academic integrity, fairness, andquality education for institutions.

A Video-based Detector for Suspicious Activity in Examination with OpenPose