Autors: Nikolova, D. V., Vladimirov, I. H., Manolova, A. H.
Title: An Experimental Analysis of Deep Learning Models for Human Activity Recognition with Synthetic Data
Keywords: Human Activity Recognition, Convolutional Neural Networks, S

Abstract: In this paper, an experimental study of state-of-theart techniques in Human Activity Recognition (HAR) is presented. Different Deep Learning algorithms, including CNNs and RNNs, are examined and compared. The experimental part is done using two real-life datasets Kinetics-400 and UCF-101 and one synthetic – SURREACT. All of them are used both for training and testing. The models – SlowFast, X3D and MViT are evaluated using accuracy top-1, and the results are identifying the best-performing combinations of dataset and model. One important question this study is trying to answer is whether a synthetic dataset can replace a real-world one. Finally, limitations and future directions are discussed, along with potential real-world applications. potential real-world applications

References

    Issue

    58th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2023, pp. 277-280, 2023, Serbia, IEEE, DOI 10.1109/ICEST58410.2023.10187343

    Copyright IEEE

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus