Generic enhanced ensemble learning with multi-level kinematic constraints for 3D action recognition
Author:
Funder
Nation Key Research and Development Program of China
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-022-11919-y.pdf
Reference60 articles.
1. Aggarwal JK, Xia L (2014) Human activity recognition from 3D data: a review. Pattern Recognit Lett 48:70–80. https://doi.org/10.1016/JPATREC201404011
2. Avola D, Cascio M, Cinque L, Foresti GL, Massaroni C, Rodola E (2020) 2-D skeleton-based action recognition via two-branch stacked LSTM-RNNs. IEEE Trans Multimed 22(10):2481–2496. https://doi.org/10.1109/TMM20192960588
3. Bian C, Feng W, Wan L, Wang S (2021) Structural knowledge distillation for efficient skeleton-based action recognition. IEEE Trans Image Process 30:2963–2976. https://doi.org/10.1109/TIP.2021.3056895
4. Chen Y, Wang L, Li C, Hou Y, Li W (2020) ConvNets-based action recognition from skeleton motion maps. Multimed Tools Appl 79 (3–4):1707–1725. https://doi.org/10.1007/s11042-019-08261-1
5. Cheng K, Zhang Y, He X, Chen W, Cheng J, Lu H (2020) Skeleton-based action recognition with shift graph convolutional network. In: Proc conf comput vision pattern recognit, pp 183–192. https://doi.org/10.1109/CVPR42600202000026
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