论文标题
识别虚拟现实中的固定和扫视
Identifying Fixation and Saccades in Virtual Reality
论文作者
论文摘要
凝视识别可以大大减少眼动数据的量，以更好地理解认知和视觉处理。凝视识别是虚拟现实中基于眼睛的互动应用的基本前提。但是，虚拟现实环境的三维特征也对现有的识别算法构成了新的挑战。基于七个评估指标和总分（七个归一化度量值的平均值），我们获得了三种现有识别算法的最佳参数（速度 - 阈值识别，分散阈值 - 阈值识别，速度和速度和分散率 - 售价 - 售价识别）以及我们修改的速度和分散型allgorith algorith algorith algorith algorith algorith algorith algorith algorith。我们将这四种算法的性能与最佳参数进行比较。结果表明，我们修改的速度和分散阈值识别表现最好。还探讨了界面复杂性对分类结果的影响。结果表明，该算法对接口复杂性不敏感。
Gaze recognition can significantly reduce the amount of eye movement data for a better understanding of cognitive and visual processing. Gaze recognition is an essential precondition for eye-based interaction applications in virtual reality. However, the three-dimensional characteristics of virtual reality environments also pose new challenges to existing recognition algorithms. Based on seven evaluation metrics and the Overall score (the mean of the seven normalized metric values), we obtain optimal parameters of three existing recognition algorithms (Velocity-Threshold Identification, Dispersion-Threshold Identification, and Velocity & Dispersion-Threshold Identification) and our modified Velocity & Dispersion-Threshold Identification algorithm. We compare the performance of these four algorithms with optimal parameters. The results show that our modified Velocity & Dispersion-Threshold Identification performs the best. The impact of interface complexity on classification results is also preliminarily explored. The results show that the algorithms are not sensitive to interface complexity.
