| Autor: |
Joseph Kihoon Kim, Junho Park, Yeon-Kug Moon, Suk-Ju Kang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 11, Pp 85799-85811 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3282185 |
| Popis: |
Many gaze tracking applications focus on use in personal devices such as mobile phones and PCs. However, gaze tracking in large screens poses challenges because with an increase in screen size, gaze tracking in the edge region decreases owing to the restricted range of human eye movement. In addition, as large screens are often exposed to the public, anyone can use the gaze tracking module. This makes it difficult to apply personalized calibration as in personal devices. To acquire accurate gaze in the edge region, we propose a novel approach–symmetric angle amplifying function–for the gaze angle, which amplifies angles when a user is looking at the edge area of the large screen. Our function is designed particularly for the case where the screen is divided into grid-shaped regions. Furthermore, for the better user experience, we optimize neural networks using the network-optimization framework and also propose a center gravity function that pulls gaze coordinates presented on the screen to the predefined center of the region to compensate for the person-wise difference in movement of the human eyes. Experimental results revealed the superiority of the proposed methods over the baseline and different types of fitting functions. The gaze tracking module serves as a part of an aggregated system and is implemented for use in autonomous vehicles. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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