RE-STNet: relational enhancement spatio-temporal networks based on skeleton action recognition
Author:
Funder
National Natural Science Foundation of China
Natural Science Foundation of Hubei Province
Key R &D plan of Hubei Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-18864-y.pdf
Reference48 articles.
1. Si C, Jing Y, Wang W, Wang L, Tan T (2020) Skeleton-based action recognition with hierarchical spatial reasoning and temporal stack learning network. Pattern Recognit 107:107511. https://doi.org/10.1016/j.patcog.2020.107511
2. Liu M, Liu H, Chen C (2017) Enhanced skeleton visualization for view invariant human action recognition. Pattern Recognit 68:346–362. https://doi.org/10.1016/j.patcog.2017.02.030
3. Ji Y, Xu F, Yang Y, Shen F, Shen H, Zheng W (2018) A large-scale rgb-d database for arbitrary-view human action recognition. In: Proceedings of the 26th ACM International Conference on Multimedia, p 1510-1518 https://doi.org/10.1145/3240508.3240675
4. Sato F, Hachiuma R, Sekii T (2023) Prompt-guided zero-shot anomaly action recognition using pretrained deep skeleton features. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 6471–6480, https://doi.org/10.1109/CVPR52729.2023.00626
5. Liu H, Wu J, Ma H, Yan Y, He R (2024) Skeleton-based multi-stream adaptive-attentional sub-graph convolution network for action recognition. Multimed. Tools Appl. 83:2935–2958. https://doi.org/10.1007/s11042-023-15778-z
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