A graph convolutional neural network model with Fisher vector encoding and channel‐wise spatial‐temporal aggregation for skeleton‐based action recognition-Reference-Cited by-同舟云学术

A graph convolutional neural network model with Fisher vector encoding and channel‐wise spatial‐temporal aggregation for skeleton‐based action recognition

Published:2022-01-17 Issue:5 Volume:16 Page:1433-1443
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET Image Processing

Author:

Tang Jun¹²,Wang Yanjiang¹^ORCID,Fu Sichao¹³,Liu Baodi¹,Liu Weifeng¹^ORCID

Affiliation:

1. College of Control Science and Engineering China University of Petroleum (East China) Qingdao China

2. College of Animation and Communication Qingdao Agricultural University Qingdao China

3. School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12422

Reference53 articles.

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3. Tran D. Wang H. Torresani L. Ray J. LeCun Y. Paluri M.:A closer look at spatiotemporal convolutions for action recognition. inProceedings of Conference on Computer Vision and Pattern Recognition pp.6450–6459.IEEE Piscataway NJ(2018)

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5. Feichtenhofer C. Pinz A. Zisserman A.:Convolutional two‐stream network fusion for video action recognition. In:Proceedings of Conference on Computer Vision and Pattern Recognition pp.1933–1941.IEEE Piscataway NJ(2016)

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