## 4 Experimental Results and Comparisons
For the wearable eye tracker designs, since the infrared-like oculography images have a higher contrast and less light, noise, and shadow interferences than the near-eye visible-light images, the method in [28] performs smaller pupil tracking and gaze estimation errors in comparison with our proposed design. However, when the comparison is focused on the setup with near-eye visible-light images, the proposed YOLOv3-tiny-based pupil tracking methods can provide the best, state-of-the-art pupil tracking accuracy. Figure 16: (**a**) Four target markers used for calibration for the training mode. (**b**) Five target points used for estimation for the testing mode. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Mean Gaze Tracking Errors, \(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{  }}}}}}}}}}}}}}\)** & **Variance (Degrees)** \\ \hline YOLOv3-tiny (original) & 2.131 & 0.249 \\ YOLOv3-tiny (one anchor) & 2.22 & 0.227 \\ YOLOv3-tiny (one anchor/one way) & 2.959 & 0.384 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparisons of gaze tracking errors among the three YOLOv3-tiny-based models for the training mode. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Mean Gaze trAcking Errors, \(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}\)** & **Variance (Degrees)** \\ \hline YOLOv3-tiny (original) & 2.434 & 0.468 \\ YOLOv3-tiny (one anchor) & 2.57 & 0.268 \\ YOLOv3-tiny (one anchor/one way) & 3.523 & 0.359 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparisons of gaze tracking errors among the three YOLOv3-tiny-based models for the testing mode. Figure 17 demonstrates the implementation result of the proposed gaze tracking design on the Nvidia Xavier embedded platform [41]. Using the embedded software implementation with Xavier, the proposed YOLOv3-tiny-based visible-light wearable gaze tracking design operates up to 20 frames per second to be suitable for practical consumer applications. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Deep-Learning-Based Methods** & **Operational Mode/Setup** & **Pupil Tracking Errors** & **Gaze Estimation Errors** \\ \hline [PERSON] et al. [22] & Visible-light mode /Non-Wearable & N/A & Mean errors are less than 7.75 degrees \\ \hline [PERSON] et al. [24] & Visible-light mode /Non-Wearable & N/A & Less than 3.64 degrees \\ \hline [PERSON] et al. [26] & Visible-light mode /Non-Wearable & N/A & Accuracy 92.4\% by 9 gaze zones \\ \hline [PERSON] et al. [29] & Visible-light mode /Non-Wearable & N/A & Mean errors are 10.8 degrees for cross-dataset /Less than 5.5 degrees for cross-person \\ \hline [PERSON] et al. [32] & Visible-light mode /Non-Wearable & N/A & Less than 5 degrees \\ \hline [PERSON] et al. [34] & Visible-light mode /Non-Wearable & N/A & Less than 2 degrees at training mode /Less than 5 degrees at testing mode \\ \hline [PERSON] et al. [35] & Visible-light mode /Non-Wearable & N/A & Less than 2 degrees \\ \hline [PERSON] et al. [36] & Infrared mode /Non-Wearable & Less than 6 pixels & Gaze-estimation bias is 0.72 degrees \\ \hline [PERSON] et al. [28] & Near-eye infrared mode/Wearable & Median accuracy is 1.0 pixel & Less than 0.5 degrees \\ \hline Proposed design & Near-eye visible-light mode/Wearable & Less than 5 pixels for the cross-person testing mode /Less than 3.5 degrees for the testing mode \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison of the deep-learning-based gaze tracking designs. Figure 17: Implementation of the proposed gaze tracking design on Nvidia Xavier embedded platform.