Real-time human action recognition using raw depth video-based recurrent neural networks
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oai:localhost:PNK-83102023-04-26T02:35:16Z Real-time human action recognition using raw depth video-based recurrent neural networks Adrián, Sánchez-Caballero David, Fuentes-Jiménez Cristina, Losada-Gutiérrez ConvLSTM HAR CC BY This work proposes and compare two different approaches for real-time human action recognition (HAR) from raw depth video sequences. Both proposals are based on the convolutional long short-term memory unit, namely ConvLSTM, with differences in the architecture and the long-term learning. The former uses a video-length adaptive input data generator (stateless) whereas the latter explores the stateful ability of general recurrent neural networks but is applied in the particular case of HAR. This stateful property allows the model to accumulate discriminative patterns from previous frames without compromising computer memory. Furthermore, since the proposal uses only depth information, HAR is carried out preserving the privacy of people in the scene, since their identities can not be recognized. Both neural networks have been trained and tested using the large-scale NTU RGB+D dataset. 2023-04-26T02:35:16Z 2023-04-26T02:35:16Z 2022 Book https://link.springer.com/article/10.1007/s11042-022-14075-5 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8310 en application/pdf Springer |
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ConvLSTM HAR |
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ConvLSTM HAR Adrián, Sánchez-Caballero David, Fuentes-Jiménez Cristina, Losada-Gutiérrez Real-time human action recognition using raw depth video-based recurrent neural networks |
description |
CC BY |
format |
Book |
author |
Adrián, Sánchez-Caballero David, Fuentes-Jiménez Cristina, Losada-Gutiérrez |
author_facet |
Adrián, Sánchez-Caballero David, Fuentes-Jiménez Cristina, Losada-Gutiérrez |
author_sort |
Adrián, Sánchez-Caballero |
title |
Real-time human action recognition using raw depth video-based recurrent neural networks |
title_short |
Real-time human action recognition using raw depth video-based recurrent neural networks |
title_full |
Real-time human action recognition using raw depth video-based recurrent neural networks |
title_fullStr |
Real-time human action recognition using raw depth video-based recurrent neural networks |
title_full_unstemmed |
Real-time human action recognition using raw depth video-based recurrent neural networks |
title_sort |
real-time human action recognition using raw depth video-based recurrent neural networks |
publisher |
Springer |
publishDate |
2023 |
url |
https://link.springer.com/article/10.1007/s11042-022-14075-5 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8310 |
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1764268033301282816 |
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8.891145 |