Our Publication

https://www.mdpi.com/1424-8220/21/17/5699

Abstract : Human action recognition in videos has become a popular research area in artificial

intelligence (AI) technology. In the past few years, this research has accelerated in areas such as

sports, daily activities, kitchen activities, etc., due to developments in the benchmarks proposed for

human action recognition datasets in these areas. However, there is little research in the benchmarking

datasets for human activity recognition in educational environments. Therefore, we developed a

dataset of teacher and student activities to expand the research in the education domain. This

paper proposes a new dataset, called EduNet, for a novel approach towards developing human

action recognition datasets in classroom environments. EduNet has 20 action classes, containing

around 7851 manually annotated clips extracted from YouTube videos, and recorded in an actual

classroom environment. Each action category has a minimum of 200 clips, and the total duration is

approximately 12 h. To the best of our knowledge, EduNet is the first dataset specially prepared for

classroom monitoring for both teacher and student activities. It is also a challenging dataset of actions

as it has many clips (and due to the unconstrained nature of the clips). We compared the performance

of the EduNet dataset with benchmark video datasets UCF101 and HMDB51 on a standard I3DResNet-

50 model, which resulted in 72.3% accuracy. The development of a new benchmark dataset

for the education domain will benefit future research concerning classroom monitoring systems. The

EduNet dataset is a collection of classroom activities from 1 to 12 standard schools.

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