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http://dx.doi.org/10.5909/JBE.2022.27.1.124

Real-time Online Study and Exam Attitude Dataset Design and Implementation  

Kim, Junsik (Department of Electronic Engineering, Kwangwoon University)
Lee, Chanhwi (Department of Electronic Engineering, Kwangwoon University)
Song, Hyok (Korea Electronics Technology Institute)
Kwon, Soonchul (Graduate School of Smart Convergence, Kwangwoon University)
Publication Information
Journal of Broadcast Engineering / v.27, no.1, 2022 , pp. 124-132 More about this Journal
Abstract
Recently, due to COVID-19, online remote classes and non-face-to-face exams have made it difficult to manage class attitudes and exam cheating. Therefore, there is a need for a system that automatically recognizes and detects the behavior of students online. Action recognition, which recognizes human action, is one of the most studied technologies in computer vision. In order to develop such a technology, data including human arm movement information and information about surrounding objects, which can be key information in online classes and exams, are needed. It is difficult to apply the existing dataset to this system because it is classified into various fields or consists of daily life action. In this paper, we propose a dataset that can classify attitudes in real-time online tests and classes. In addition, it shows whether the proposed dataset is correctly constructed through comparison with the existing action recognition dataset.
Keywords
dataset construction; online study and exam attitude; deep neural network;
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