Cheating video description based on sequences of gestures

Arinaldi, Ahmad; Fanany, Mohamad Ivan

doi:10.1109/icoict.2017.8074679

Cited by 13 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For online exams, candidates would need to have a camera to participate. Arinaldi and Fanany [9] utilized a 3DCNN model, XGBoost, and long short-term memory (LSTM) to process action sequences and textually describe videos on a dataset of 71 cheating videos, including actions such as document exchange, looking at nearby individuals, opening documents, and talking. The gesture recognition model achieved an accuracy score of 81.11%, while the language model utilizing LSTM achieved a score of 95.3%.…”

Section: Introductionmentioning

confidence: 99%

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

Tran,

Nguyen,

et al. 2023

IJEECS

View full text Add to dashboard Cite

<p>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.</p>

show abstract

Section: Introductionmentioning

confidence: 99%

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

Tran,

Nguyen,

et al. 2023

IJEECS

View full text Add to dashboard Cite

show abstract

“…In particular, the traditional methods of invigilation during the examination to detect unfair means require manual observation of students. An invigilator cannot monitor all the students and may lose attention over time, allowing pupils to engage in cheating activities [8]. Thus, there is a need for automated and intelligent video-based suspicious activity detection systems that may help analyses, detect and minimize unwanted acts resulting in unfair means.…”

Section: Introductionmentioning

confidence: 99%

Automatic Unusual Activities Recognition Using Deep Learning in Academia

Ramzan¹,

Abid²,

Awan³

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

In the current era, automatic surveillance has become an active research problem due to its vast real-world applications, particularly for maintaining law and order. A continuous manual monitoring of human activities is a tedious task. The use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few years. Various computer vision techniques have been applied for observation and surveillance of real-world activities. This research study focuses on detecting and recognizing unusual activities in an academic situation such as examination halls, which may help the invigilators observe and restrict the students from cheating or using unfair means. To the best of our knowledge, this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual activities. The proposed model has been named Automatic Unusual Activity Recognition (AUAR), which employs motion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional Neural Network algorithm with diverse configurations. The evaluation using standard performance measures confirm that the AUAR model outperforms the already proposed approaches for unusual activity recognition. Apart from evaluating the proposed model on the examination dataset, we also apply AUAR on Violent and Movies datasets, widely used in the relevant literature to detect suspicious activities. The results reveal that AUAR performs well on various data sets compared to existing state-of-the-art models.

show abstract