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.
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