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- W4385235624 abstract "In this paper, an experimental study of state-of-the-art 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." @default.
- W4385235624 created "2023-07-26" @default.
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- W4385235624 date "2023-06-29" @default.
- W4385235624 modified "2023-10-17" @default.
- W4385235624 title "An Experimental Analysis of Deep Learning Models for Human Activity Recognition with Synthetic Data" @default.
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- W4385235624 doi "https://doi.org/10.1109/icest58410.2023.10187343" @default.
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