A Stable Deep Learning Model for Recognizing Human Actions in Noisy Videos

The widespread application and development of technology have significantly improved people's lives. In recent years, deep learning has emerged as a powerful branch of artificial intelligence, enabling the creation of intelligent software and robots that assist with various daily tasks. Deep Learning, a subtype of Complex Artificial Neural Networks (ANNs) based on machine learning, is commonly used for accurate human behavior detection from smartphone data. In this paper, we propose a model to identify human activity using ConvLSTM, DenseNet, and LRCN. The statistical model is built on a substantial dataset of videos sourced from publicly accessible datasets UCF50 and HMDB51, which cover a wide range of activity variables. Our results demonstrate that the DenseNet-based model outperforms other models in terms of accuracy.