PerimetryNet: A multiscale fine grained deep network for three-dimensional eye gaze estimation using visual field analysis
PerimetryNet: A multiscale fine grained deep network for three-dimensional eye gaze estimation using visual field analysis
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DOI：
10.1002/cav.2141
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发表时间：
2023-02-13
影响因子：
1.1
通讯作者：
Wang，Zhao
中科院分区：
文献类型：
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作者：
Yu，Shuqing;Wang，Zhihao;Wang，Zhao
三维视线估计旨在揭示一个人在看什么，这在识别用户在方向、注意力和交互方面的兴趣点方面起着重要作用。基于外观的注视估计方法可以从商品硬件提供相对不受约束的注视跟踪。受医学视野检查的启发，我们提出了一个多尺度框架与视野分析分支，以提高估计精度。该模型以特征金字塔为基础，预测视野，以帮助视线估计。特别是，我们分析了多尺度分量和视野分支对具有挑战性的基准数据集：MPIIGaze和EYEDIAP的影响。基于这些研究，我们提出的PerimetryNet显著优于最先进的方法。此外，多尺度机制和视野分支可以容易地应用于现有的网络架构，用于注视估计。相关代码可在公共存储库https://github.com/gazeEs/PerimetryNet上获得。
Three‐dimensional gaze estimation aims to reveal where a person is looking, which plays an important role in identifying users' point‐of‐interest in terms of the direction, attention and interactions. Appearance‐based gaze estimation methods could provide relatively unconstrained gaze tracking from commodity hardware. Inspired by medical perimetry test, we have proposed a multiscale framework with visual field analysis branch to improve estimation accuracy. The model is based on the feature pyramids and predicts vision field to help gaze estimation. In particular, we analysis the effect of the multiscale component and the visual field branch on challenging benchmark datasets: MPIIGaze and EYEDIAP. Based on these studies, our proposed PerimetryNet significantly outperforms state‐of‐the‐art methods. In addition, the multiscale mechanism and visual field branch can be easily applied to existing network architecture for gaze estimation. Related code would be available at public repository https://github.com/gazeEs/PerimetryNet.