Adaptive occlusion hybrid second-order attention network for head pose estimation-Reference-Cited by-同舟云学术

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Adaptive occlusion hybrid second-order attention network for head pose estimation

Published:2023-08-07 Issue:2 Volume:15 Page:667-683
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Fu Qi,Xie Kai^ORCID,Wen Chang,He Jianbiao,Zhang Wei,Tian Hongling,Yang Sheng

Funder

National Natural Science Foundation of China

Natural Science Foundation of Xinjiang Uygur Autonomous Region

Teaching and Research Fund of Yangtze University

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-023-01933-3.pdf

Reference63 articles.

1. Murphy-Chutorian E, Trivedi MM (2009) Head pose estimation in computer vision: a survey. IEEE Trans Pattern Anal Mach Intell 31(4):607–626. https://doi.org/10.1109/TPAMI.2008.106

2. Wang K, Zhao R, Ji Q (2018) Human Computer Interaction with Head Pose, Eye Gaze and Body Gestures. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp 789-789. https://doi.org/10.1109/FG.2018.00126

3. Li Y, Li J, Jiang X et al (2019) A Driving Attention Detection Method Based on Head Pose. In: 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation(SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp 483-490. https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00124

4. Bosch N, Dmello SK (2021) Automatic detection of mind wandering from video in the lab and in the classroom. IEEE Trans Affect Comput 12(4):974–988. https://doi.org/10.1109/TAFFC.2019.2908837

5. Zhuang Z, Tao H, Chen Y et al (2022) An Optimal Iterative Learning Control Approach for Linear Systems With Nonuniform Trial Lengths Under Input Constraints. IEEE Trans on Syst, Man, and Cybern: Syst 1–13. https://doi.org/10.1109/TSMC.2022.3225381

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1. Multi-modal 6-DoF object pose tracking: integrating spatial cues with monocular RGB imagery;International Journal of Machine Learning and Cybernetics;2024-08-30

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