  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.
