Skeleton-based human action recognition with global context-aware attention LSTM networks
Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, long short-term memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/142304 |
| _version_ | 1826127059914588160 |
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| author | Liu, Jun Wang, Gang Duan, Ling-Yu Abdiyeva, Kamila Kot, Alex Chichung |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Liu, Jun Wang, Gang Duan, Ling-Yu Abdiyeva, Kamila Kot, Alex Chichung |
| author_sort | Liu, Jun |
| collection | NTU |
| description | Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, long short-term memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, global context-aware attention LSTM, for skeleton-based action recognition, which is capable of selectively focusing on the informative joints in each frame by using a global context memory cell. To further improve the attention capability, we also introduce a recurrent attention mechanism, with which the attention performance of our network can be enhanced progressively. Besides, a two-stream framework, which leverages coarse-grained attention and fine-grained attention, is also introduced. The proposed method achieves state-of-the-art performance on five challenging datasets for skeleton-based action recognition. |
| first_indexed | 2024-10-01T07:02:35Z |
| format | Journal Article |
| id | ntu-10356/142304 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:02:35Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1423042020-06-18T08:46:12Z Skeleton-based human action recognition with global context-aware attention LSTM networks Liu, Jun Wang, Gang Duan, Ling-Yu Abdiyeva, Kamila Kot, Alex Chichung School of Electrical and Electronic Engineering Engineering::Computer science and engineering Action Recognition Long Short-term Memory Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, long short-term memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, global context-aware attention LSTM, for skeleton-based action recognition, which is capable of selectively focusing on the informative joints in each frame by using a global context memory cell. To further improve the attention capability, we also introduce a recurrent attention mechanism, with which the attention performance of our network can be enhanced progressively. Besides, a two-stream framework, which leverages coarse-grained attention and fine-grained attention, is also introduced. The proposed method achieves state-of-the-art performance on five challenging datasets for skeleton-based action recognition. NRF (Natl Research Foundation, S’pore) 2020-06-18T08:46:12Z 2020-06-18T08:46:12Z 2017 Journal Article Liu, J., Wang, G., Duan, L.-Y., Abdiyeva, K., & Kot, A. C. (2018). Skeleton-based human action recognition with global context-aware attention LSTM networks. IEEE Transactions on Image Processing, 27(4), 1586-1599. doi:10.1109/TIP.2017.2785279 1057-7149 https://hdl.handle.net/10356/142304 10.1109/TIP.2017.2785279 29324413 2-s2.0-85039779403 4 27 1586 1599 en IEEE Transactions on Image Processing © 2017 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Action Recognition Long Short-term Memory Liu, Jun Wang, Gang Duan, Ling-Yu Abdiyeva, Kamila Kot, Alex Chichung Skeleton-based human action recognition with global context-aware attention LSTM networks |
| title | Skeleton-based human action recognition with global context-aware attention LSTM networks |
| title_full | Skeleton-based human action recognition with global context-aware attention LSTM networks |
| title_fullStr | Skeleton-based human action recognition with global context-aware attention LSTM networks |
| title_full_unstemmed | Skeleton-based human action recognition with global context-aware attention LSTM networks |
| title_short | Skeleton-based human action recognition with global context-aware attention LSTM networks |
| title_sort | skeleton based human action recognition with global context aware attention lstm networks |
| topic | Engineering::Computer science and engineering Action Recognition Long Short-term Memory |
| url | https://hdl.handle.net/10356/142304 |
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