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An Improved Tiered Head Pose Estimation Network with Self-Adjust Loss Function

As an important task in computer vision, head pose estimation has been widely applied in both academia and industry. However, there remains two challenges in the field of head pose estimation: (1) even given the same task (e.g., tiredness detection), the existing algorithms usually consider the esti...

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Autores principales: Zhu, Xiaoliang, Yang, Qiaolai, Zhao, Liang, Dai, Zhicheng, He, Zili, Rong, Wenting, Sun, Junyi, Liu, Gendong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9320982/
https://www.ncbi.nlm.nih.gov/pubmed/35885197
http://dx.doi.org/10.3390/e24070974
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author Zhu, Xiaoliang
Yang, Qiaolai
Zhao, Liang
Dai, Zhicheng
He, Zili
Rong, Wenting
Sun, Junyi
Liu, Gendong
author_facet Zhu, Xiaoliang
Yang, Qiaolai
Zhao, Liang
Dai, Zhicheng
He, Zili
Rong, Wenting
Sun, Junyi
Liu, Gendong
author_sort Zhu, Xiaoliang
collection PubMed
description As an important task in computer vision, head pose estimation has been widely applied in both academia and industry. However, there remains two challenges in the field of head pose estimation: (1) even given the same task (e.g., tiredness detection), the existing algorithms usually consider the estimation of the three angles (i.e., roll, yaw, and pitch) as separate facets, which disregard their interplay as well as differences and thus share the same parameters for all layers; and (2) the discontinuity in angle estimation definitely reduces the accuracy. To solve these two problems, a THESL-Net (tiered head pose estimation with self-adjust loss network) model is proposed in this study. Specifically, first, an idea of stepped estimation using distinct network layers is proposed, gaining a greater freedom during angle estimation. Furthermore, the reasons for the discontinuity in angle estimation are revealed, including not only labeling the dataset with quaternions or Euler angles, but also the loss function that simply adds the classification and regression losses. Subsequently, a self-adjustment constraint on the loss function is applied, making the angle estimation more consistent. Finally, to examine the influence of different angle ranges on the proposed model, experiments are conducted on three popular public benchmark datasets, BIWI, AFLW2000, and UPNA, demonstrating that the proposed model outperforms the state-of-the-art approaches.
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spelling pubmed-93209822022-07-27 An Improved Tiered Head Pose Estimation Network with Self-Adjust Loss Function Zhu, Xiaoliang Yang, Qiaolai Zhao, Liang Dai, Zhicheng He, Zili Rong, Wenting Sun, Junyi Liu, Gendong Entropy (Basel) Article As an important task in computer vision, head pose estimation has been widely applied in both academia and industry. However, there remains two challenges in the field of head pose estimation: (1) even given the same task (e.g., tiredness detection), the existing algorithms usually consider the estimation of the three angles (i.e., roll, yaw, and pitch) as separate facets, which disregard their interplay as well as differences and thus share the same parameters for all layers; and (2) the discontinuity in angle estimation definitely reduces the accuracy. To solve these two problems, a THESL-Net (tiered head pose estimation with self-adjust loss network) model is proposed in this study. Specifically, first, an idea of stepped estimation using distinct network layers is proposed, gaining a greater freedom during angle estimation. Furthermore, the reasons for the discontinuity in angle estimation are revealed, including not only labeling the dataset with quaternions or Euler angles, but also the loss function that simply adds the classification and regression losses. Subsequently, a self-adjustment constraint on the loss function is applied, making the angle estimation more consistent. Finally, to examine the influence of different angle ranges on the proposed model, experiments are conducted on three popular public benchmark datasets, BIWI, AFLW2000, and UPNA, demonstrating that the proposed model outperforms the state-of-the-art approaches. MDPI 2022-07-14 /pmc/articles/PMC9320982/ /pubmed/35885197 http://dx.doi.org/10.3390/e24070974 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhu, Xiaoliang
Yang, Qiaolai
Zhao, Liang
Dai, Zhicheng
He, Zili
Rong, Wenting
Sun, Junyi
Liu, Gendong
An Improved Tiered Head Pose Estimation Network with Self-Adjust Loss Function
title An Improved Tiered Head Pose Estimation Network with Self-Adjust Loss Function
title_full An Improved Tiered Head Pose Estimation Network with Self-Adjust Loss Function
title_fullStr An Improved Tiered Head Pose Estimation Network with Self-Adjust Loss Function
title_full_unstemmed An Improved Tiered Head Pose Estimation Network with Self-Adjust Loss Function
title_short An Improved Tiered Head Pose Estimation Network with Self-Adjust Loss Function
title_sort improved tiered head pose estimation network with self-adjust loss function
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9320982/
https://www.ncbi.nlm.nih.gov/pubmed/35885197
http://dx.doi.org/10.3390/e24070974
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