UDC 303.724.32
DOI: 10.36871/2618-9976.2024.01.002
Authors
Eugene Yu. Shchetinin,
Professor, Department of Mathematics, Faculty of Information Technologies and Big Data Analysis,
Financial University under the Government of the Russian Federation, Moscow, Russia
Abstract
Human activity recognition is one of the actively developing areas
of artificial intelligence that can be implemented in learning
environments to improve safety, efficiency and quality of education.
Deep learning transfer is a widespread method of training
neural networks and has proven its effectiveness in such complex
tasks as digital image and video processing.
In this paper, we propose a model for recognizing different student
activities using pretrained
models including VGG16,
ResNet50,
DenseNet201, EfficientNetB0 and Xception, which are
then trained using the method proposed in the paper on a Class
box image set created by the authors.
The EfficientNetB0 model showed the maximum performance
among the considered models and achieved an accuracy of 94,25%.
The system proposed in this study aims to create a safer and more
productive learning environment for students and teachers.
Keywords
Recognition, Human activity, Transfer learning classroom, Classification
