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通过防范学生或监考人员的作弊行为来维护考试公正性。然而，考试中的作弊现象日益猖獗，严重损害了考试的公信力。传统依赖监考人员逐一巡视的监控方式既不可行也效率低下。为解决该问题，需要持续录制考试过程以监测学生的可疑行为。但考试录像通常时长过长，监考人员难以有效分析，且疲劳因素可能导致重要细节被遗漏。为扩大监控覆盖面，监考人员可采用固定式顶置摄像头或可穿戴摄像设备。本文提出一种基于自动化视频分析的框架，能够高效可靠地检测考试中的可疑行为。我们利用OpenPose框架与卷积神经网络（CNN）识别考试过程中学生传递物品的行为。该检测系统对预防作弊、维护学术诚信、促进教育公平与保障机构教育质量具有关键意义。