<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Sight is the main source for humans to obtain information from the outside world. Due to the structure of the human eye [<xref ref-type="bibr" rid="j_ijanmc-2022-0009_ref_001">1</xref>], the range of human sight is limited. For this reason, people need to constantly move their line of sight when observing the surrounding environment and the target, and the movement of the sight is based on the coordinated movement of the head and the eye[<xref ref-type="bibr" rid="j_ijanmc-2022-0009_ref_002">2</xref>]. Therefore, the key issue for gaze research is how to correctly establish the relationship between head-eye movement and gaze movement. Taking the simulated flight environment as the research background, this paper collects a large number of head-eye images through the designed “three-camera and eight-light source” head-eye data acquisition platform, and proposes a gaze estimation method based on the combination of appearance and features, which effectively combines The relationship of head-eye coordination movement. Then, the ResNet-18 deep residual network structure and the traditional BP neural network structure are used to complete the effective fusion of the head pose and human eye features in the process of capturing the sight target, so as to realize the accurate estimation of the sight drop point, and its average accuracy up to 89.9%.</p>
</abstract>

Sight is the main source for humans to obtain information from the outside world. Due to the structure of the human eye [1], the range of human sight is limited. For this reason, people need to constantly move their line of sight when observing the surrounding environment and the target, and the movement of the sight is based on the coordinated movement of the head and the eye[2]. Therefore, the key issue for gaze research is how to correctly establish the relationship between head-eye movement and gaze movement. Taking the simulated flight environment as the research background, this paper collects a large number of head-eye images through the designed “three-camera and eight-light source” head-eye data acquisition platform, and proposes a gaze estimation method based on the combination of appearance and features, which effectively combines The relationship of head-eye coordination movement. Then, the ResNet-18 deep residual network structure and the traditional BP neural network structure are used to complete the effective fusion of the head pose and human eye features in the process of capturing the sight target, so as to realize the accurate estimation of the sight drop point, and its average accuracy up to 89.9%.

Research on the Estimation of Gaze Location for Head-eye Coordination Movement

School of Computer Science and Engineering

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Research on the Estimation of Gaze Location for Head-eye Coordination Movement"}

Sight is the main source for humans to obtain information from the outside world. Due to the structure of the human eye [1], the range of human sight is...

Research on the Estimation of Gaze Location for Head-eye Coordination Movement

Online veröffentlicht: 28. Mai 2023

Seitenbereich: 116 - 131

DOI: https://doi.org/10.2478/ijanmc-2022-0009

Schlüsselwörter
Head-Eye Coordination, Gaze Estimation Method, Experimental Platform Design, Deep Residual Networks

© 2022 Qiyou Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Research on the Estimation of Gaze Location for Head-eye Coordination Movement

Online veröffentlicht: 28. Mai 2023

Seitenbereich: 116 - 131

DOI: https://doi.org/10.2478/ijanmc-2022-0009

SchlüsselwörterHead-Eye Coordination, Gaze Estimation Method, Experimental Platform Design, Deep Residual Networks

© 2022 Qiyou Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Head-Eye Coordination, Gaze Estimation Method, Experimental Platform Design, Deep Residual Networks