Multi-cue based 3D residual network for action recognition-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Multi-cue based 3D residual network for action recognition

Published:2020-09-02 Issue:10 Volume:33 Page:5167-5181
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Zong Ming,Wang Ruili,Chen Zhe,Wang Maoli^ORCID,Wang Xun,Potgieter Johan

Funder

Fundamental Research Funds for the Central Universities

Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Major Basic Research of Shandong Natural Science Foundation

Key Research and Development Plan of Shandong Province

Major agricultural application technology innovation project of Shandong Province

National Key Research and Development Program of China

China Scholarship Council

New Zealand-China Doctoral Research Scholarships Program

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-020-05313-8.pdf

Reference62 articles.

1. Arunnehru J, Chamundeeswari G, Prasanna Bharathi S (2018) Human action recognition using 3D convolutional neural networks with 3D motion cuboids in surveillance videos. Procedia Computer Sci 133:471–477

2. Baccouche M, Mamalet F, Wolf C, Garcia C, Baskurt A (2011) Sequential deep learning for human action recognition. In: International workshop on human behavior understanding, pp 29–39. Springer, Berlin

3. Brox T, Bruhn A, Papenberg N, Weickert J (2004) High accuracy optical flow estimation based on a theory for warping. In: European conference on computer vision, pp 25–36. Springer, Berlin

4. Carreira J, Zisserman A (2017) Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6299–6308

5. Chen Chenglizhao, Li Shuai, Wang Yongguang, Qin Hong, Hao Aimin (2017) Video saliency detection via spatial-temporal fusion and low-rank coherency diffusion. IEEE Trans Image Process 26(7):3156–3170

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-stream ternary enhanced graph convolutional network for skeleton-based action recognition;Neural Computing and Applications;2023-06-14

2. Research on 3D Convolutional Neural Network and Its Application to Video Understanding;J ELECTRON INF TECHN;2023

3. Spatial and temporal saliency based four-stream network with multi-task learning for action recognition;Applied Soft Computing;2023-01

4. MobileACNet: ACNet-Based Lightweight Model for Image Classification;Image and Vision Computing;2023

5. FT-HID: a large-scale RGB-D dataset for first- and third-person human interaction analysis;Neural Computing and Applications;2022-10-07

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3