Deep spatiotemporal LSTM network with temporal pattern feature for 3D human action recognition-Reference-Cited by-同舟云学术

Deep spatiotemporal LSTM network with temporal pattern feature for 3D human action recognition

Published:2019-04-22 Issue:3 Volume:35 Page:535-554
ISSN:0824-7935
Container-title:Computational Intelligence
language:en
Short-container-title:Computational Intelligence

Author:

Wu Yirui¹²^ORCID,Wei Lianglei³,Duan Yucong⁴

Affiliation:

1. College of Computer and InformationHohai University Nanjing China

2. National Key Lab for Novel Software TechnologyNanjing University Nanjing China

3. Ruiting Network Technology Company Shanghai China

4. College of Information and TechnologyHainan University Haikou China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Natural Science Foundation of Jiangsu Province

Publisher

Wiley

Subject

Artificial Intelligence,Computational Mathematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/coin.12207

Reference68 articles.

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3. ChangX YuY‐L YangY XingEP.They are not equally reliable: semantic event search using differentiated concept classifiers. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR);2016;Las Vegas NV.

4. WangH ChangX ShiL YangY ShenY‐D.Uncertainty sampling for action recognition via maximizing expected average precision. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence;2018;Stockholm Sweden.

5. Effective 3D action recognition using EigenJoints

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