Student Activity Recognition in Classroom Environments using Transfer Learning

TL;DR

This study develops a transfer learning-based system to recognize student activities in classrooms, utilizing a newly created dataset and pretrained models, with Xception achieving the highest accuracy of 93%.

Contribution

It introduces a novel classroom activity dataset and applies transfer learning with multiple models to improve activity recognition accuracy.

Findings

Xception achieved 93% accuracy on the new dataset.

Transfer learning models outperform traditional methods.

The system enhances safety and productivity in classrooms.

Abstract

The recent advances in artificial intelligence and deep learning facilitate automation in various applications including home automation, smart surveillance systems, and healthcare among others. Human Activity Recognition is one of its emerging applications, which can be implemented in a classroom environment to enhance safety, efficiency, and overall educational quality. This paper proposes a system for detecting and recognizing the activities of students in a classroom environment. The dataset has been structured and recorded by the authors since a standard dataset for this task was not available at the time of this study. Transfer learning, a widely adopted method within the field of deep learning, has proven to be helpful in complex tasks like image and video processing. Pretrained models including VGG-16, ResNet-50, InceptionV3, and Xception are used for feature extraction and classification tasks. Xception achieved an accuracy of 93%, on the novel classroom dataset, outperforming the other three models in consideration. The system proposed in this study aims to introduce a safer and more productive learning environment for students and educators.