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

arXiv: https://arxiv.org/abs/2307.11413 · HTML: https://mlsanity.com/paper/2307.11413/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 21 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose](https://mlsanity.com/paper/1811.12004/) (2018)
- [Pose Trainer: Correcting Exercise Posture using Pose Estimation](https://mlsanity.com/paper/2006.11718/) (2020)
- [Good proctor or "Big Brother"? AI Ethics and Online Exam Supervision Technologies](https://mlsanity.com/paper/2011.07647/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Two-Stage, Object-Centric Deep Learning Framework for Robust Exam Cheating Detection](https://mlsanity.com/paper/2604.16234/) (2026)

## Abstract (arXiv)

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.