Automatic Invigilation Using Computer Vision

Malhotra, Manit; Chhabra, Indu

doi:10.2991/ahis.k.210913.017

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…Radwan et al [25] presented an intelligent approach using deep learning to efficiently detect suspicious behavior in real-time examination. Similarly, Malhotra and Chhabra [26] suggested a model for exam invigilation using deep learning and computer vision. Cheating detection was done based on the students' neck and head movement.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent solution for automatic online exam monitoring

Moukhliss

Hilali

Belhadaoui

2023

IJECE

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E-learning has shown significant growth in recent years due to its unavoidable benefits in unexpected situations such as the coronavirus disease 2019 (COVID-19) pandemic. Indeed, online exam is a very important component of an online learning program. It allows higher education institutions to assess student learning outcomes. However, cheating in exams is a widespread phenomenon worldwide, which creates several challenges in terms of integrity, reliability and security of online examinations. In this study, we propose a continuous authentication system for online exam. Our intelligent inference system based on machine learning algorithms and rules, detects continuously any inappropriate behavior in order to limit and prevent fraud. The proposed model includes several modules to enhance security, namely the registration module, the continuous students’ identity verification and control module, the live video stream and the end-to-end sessions recording.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent solution for automatic online exam monitoring

Moukhliss

Hilali

Belhadaoui

2023

IJECE

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“…The system's average accuracy is 79% across all categories. Malhotra and Chhabra [18] demonstrated a computer vision-based automated approach for detecting aberrant activity during examinations. The goal of this project is to use closedcircuit television (CCTV) cameras to watch for suspicious conduct in students during physical exams.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network (CNN) Model to Mobile Remote Surveillance System for Home Security

Ayad¹,

Al-Khalidi²

2023

IJCDS

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Every citizen wants to eliminate any potential threat to themselves or their belongings. A proper and modern surveillance system is necessary given the enormous increase in the security needs of both persons and organizations. Remote surveillance is one of the major issues which attracted the attention of researchers recently. Surveillance cameras, commonly known as closed-circuit television (CCTV) have grown rapidly in popularity over the last few decades. Nowadays, video surveillance is quite essential. It greatly aids in reducing crime rates and can be used to keep track of the condition of buildings. In this paper, a remote surveillance system using the Convolutional Neural Networks (CNN) model was proposed. This system consists of a camera that takes more than one image of the object that passes in front of it and sends these images to the mobile phone of the intended person (the owner of the place). Images taken by the security camera may be analyzed using the CNN model depending on the region of interest for detection. The system will be delivering an alarm to the user depending on intelligent detection depending on a deep learning approach that can improve a smart home automation structure by detection people. The results of the experiments demonstrate a high degree of accuracy in detecting human beings , the accuracy of the system reached 100% in an ideal time.

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“…In addition to cheating in online exams, there are also many research works that identify direct anomalous behaviors in the classroom. Malhotra and Chhabra [12] suggested an automatic system for recognizing anomalous behaviors. They used YOLOv3 [13] combined with ShuffleNet [14] and achieving an average precision (AP) measure of 88.03% for the detection of cheating in the classroom environment.…”

Section: Introductionmentioning

confidence: 99%

Exploring the potential of skeleton and machine learning in classroom cheating detection

Tran,

Nguyen,

et al. 2023

IJEECS

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In recent years, there have been certain drawbacks in the behavior of students in school cultural activities, especially the frequent occurrence of cheating in exams. Although many manual cheating detection methods have been applied, cheating behaviors have become increasingly sophisticated and difficult to detect. This makes manual cheating detection methods unable to completely prevent cheating in exams. Therefore, applying advanced technologies to automatically detect cheating cases becomes necessary to help supervisors better control the exam process. In this study, we propose a model architecture to monitor students’ cheating activities during exams. Firstly, we apply YOLO - Pose to detect skeleton keypoints, which are then passed through machine learning models such as support vector machine (SVM), decision tree (DT) classifier, random forest (RF), extreme gradient bo osting (XGBoost), and propose the Ac Long short - term memory (LSTM) to detect cheating behavior. The experimental results show the feasibility of the model on various metrics. The model is tested on a dataset of cheating behaviors in the classroom, which is designed to simulate paper - based exams. Moreover, the results showed that the proposed method can detect cheating behavior in a short amount of time and can be deployed for real - world applications.

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Automatic Invigilation Using Computer Vision

Cited by 8 publications

References 24 publications

Intelligent solution for automatic online exam monitoring

Intelligent solution for automatic online exam monitoring

Convolutional Neural Network (CNN) Model to Mobile Remote Surveillance System for Home Security

Exploring the potential of skeleton and machine learning in classroom cheating detection

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