Action recognition method based on multi-stream attention-enhanced recursive graph convolution-Reference-Cited by-同舟云学术

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Action recognition method based on multi-stream attention-enhanced recursive graph convolution

Published:2024-08-07 Issue:20 Volume:54 Page:10133-10147
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Wang Huaijun,Bai Bingqian,Li Junhuai^ORCID,Ke Hui,Xiang Wei

Funder

National Natural Science Foundation of China

Doctoral Innovation Foundation of Xi’an University of Technology

Natural Science Foundation of Shaanxi Province of China

Key research and development program of Shaanxi Province

Xi ’an Science and Technology Planning Project

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10489-024-05719-0.pdf

Reference48 articles.

1. Saroja M, Baskaran K, Priyanka P (2021) Human pose estimation approaches for human activity recognition. In: 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), pp 1–4

2. Wang H, Yang J, Cui C, Tu P, Li J, Fu B, Xiang W (2024) Human activity recognition based on local linear embedding and geodesic flow kernel on grassmann manifolds. Expert Syst Appl 241:122696

3. Zheng B, Chen L, Wu M, Pedrycz W, Hirota K (2022) Skeleton-based action recognition using two-stream graph convolutional network with pose refinement. In: 2022 41st Chinese Control Conference (CCC), pp 6353–6356

4. Aggarwal JK, Xia L (2014) Human activity recognition from 3d data: a review. Pattern Recognit Lett 48:70–80

5. Liu K, Gao L, Khan NM, Qi L, Guan L (2021) Integrating vertex and edge features with graph convolutional networks for skeleton-based action recognition. Neurocomputing 466:190–201

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