Examinations are a crucial part of the learning process, and academic institutions invest significant resources into maintaining their integrity by preventing cheating from students or facilitators. However, cheating has become rampant in examination setups, compromising their integrity. The traditional method of relying on invigilators to monitor every student is impractical and ineffective. To address this issue, there is a need to continuously record exam sessions to monitor students for suspicious activities. However, these recordings are often too lengthy for invigilators to analyze effectively, and fatigue may cause them to miss significant details. To widen the coverage, invigilators could use fixed overhead or wearable cameras. This paper introduces a framework that uses automation to analyze videos and detect suspicious activities during examinations efficiently and effectively. We utilized the OpenPose framework and Convolutional Neural Network (CNN) to identify students exchanging objects during exams. This detection system is vital in preventing cheating and promoting academic integrity, fairness, and quality education for institutions.

翻译：考试是学习过程中的关键环节，学术机构投入大量资源以维护考试公平性，防止学生或监考人员的作弊行为。然而，考试场景中作弊现象日益猖獗，严重损害了考试的公正性。传统依赖监考人员逐一监督每位考生的方法既不实用也低效。为解决这一问题，需要持续录制考试过程以监测学生的可疑行为。但录制视频往往过于冗长，监考人员难以有效分析，且疲劳可能导致其遗漏关键细节。为扩大监测范围，监考人员可使用固定俯拍摄像头或可穿戴摄像头。本文提出一个框架，通过自动化分析视频高效检测考试中的可疑行为。我们利用OpenPose框架和卷积神经网络（CNN）识别考生在考试中传递物品的行为。该检测系统对防止作弊、维护学术诚信、促进公平公正及保障教育质量具有关键意义。