Jindal Swati, Kaur Harsimran, Manduchi Roberto
University of California, Santa Cruz, USA.
Proc Eye Track Res Appl Symp. 2022 Jun;2022. doi: 10.1145/3517031.3529643. Epub 2022 Jun 8.
Modern appearance-based gaze tracking algorithms require vast amounts of training data, with images of a viewer annotated with "ground truth" gaze direction. The standard approach to obtain gaze annotations is to ask subjects to fixate at specific known locations, then use a head model to determine the location of "origin of gaze". We propose using an IR gaze tracker to generate gaze annotations in natural settings that do not require the fixation of target points. This requires prior geometric calibration of the IR gaze tracker with the camera, such that the data produced by the IR tracker can be expressed in the camera's reference frame. This contribution introduces a simple tracker/camera calibration procedure based on the PnP algorithm and demonstrates its use to obtain a full characterization of gaze direction that can be used for ground truth annotation.
现代基于外观的注视跟踪算法需要大量的训练数据，即带有“真实”注视方向注释的观看者图像。获取注视注释的标准方法是要求受试者注视特定的已知位置，然后使用头部模型来确定“注视原点”的位置。我们建议使用红外注视跟踪器在不需要注视目标点的自然环境中生成注视注释。这需要对红外注视跟踪器与相机进行预先的几何校准，以便红外跟踪器产生的数据能够在相机的参考系中表示。本文介绍了一种基于PnP算法的简单跟踪器/相机校准程序，并演示了其用于获得可用于真实注释的注视方向完整特征的用途。
