Deep Object Detector With Attentional Spatiotemporal LSTM for Space Human–Robot Interaction-Reference-Cited by-同舟云学术

Deep Object Detector With Attentional Spatiotemporal LSTM for Space Human–Robot Interaction

Published:2022-08 Issue:4 Volume:52 Page:784-793
ISSN:2168-2291
Container-title:IEEE Transactions on Human-Machine Systems
language:
Short-container-title:IEEE Trans. Human-Mach. Syst.

Author:

Yu Jiahui¹^ORCID,Gao Hongwei²,Chen Yongquan¹^ORCID,Zhou Dalin³^ORCID,Liu Jinguo⁴^ORCID,Ju Zhaojie³^ORCID

Affiliation:

1. Institute of Robotics and Intelligent Manufacturing and School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China

2. School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, China

3. School of Computing, University of Portsmouth, Portsmouth, U.K.

4. State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China

Funder

Shenzhen Science and Technology Program

Institute of Artificial Intelligence and Robotics for Society

European Regional Development Fund

National Key R&D Program of China

CAS Interdisciplinary Innovation Team

LiaoNing Province Higher Education Innovative Talents Program Support

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Human-Computer Interaction,Signal Processing,Control and Systems Engineering,Human Factors and Ergonomics

Link

http://xplorestaging.ieee.org/ielx7/6221037/9829905/09709637.pdf?arnumber=9709637

Reference52 articles.

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3. R-FCN: Object detection via region-based fully convolutional networks;dai;Proc Adv Neural Inf Process Syst,0

4. Vision for Intelligent Vehicles & Applications (VIVA): Face detection and head pose challenge

5. EgoGesture: A New Dataset and Benchmark for Egocentric Hand Gesture Recognition

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