Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.
Sensors (Basel). 2019 Jan 8;19(1):216. doi: 10.3390/s19010216.
Proper calibration of eye movement signal registered by an eye tracker seems to be one of the main challenges in popularizing eye trackers as yet another user-input device. Classic calibration methods taking time and imposing unnatural behavior on eyes must be replaced by intelligent methods that are able to calibrate the signal without conscious cooperation by the user. Such an implicit calibration requires some knowledge about the stimulus a user is looking at and takes into account this information to predict probable gaze targets. This paper describes a possible method to perform implicit calibration: it starts with finding probable fixation targets (PFTs), then it uses these targets to build a mapping-probable gaze path. Various algorithms that may be used for finding PFTs and mappings are presented in the paper and errors are calculated using two datasets registered with two different types of eye trackers. The results show that although for now the implicit calibration provides results worse than the classic one, it may be comparable with it and sufficient for some applications.
看来，要将眼动仪作为另一种用户输入设备普及开来，正确校准眼动信号是主要挑战之一。需要用能够在用户无意识配合的情况下校准信号的智能方法，取代费时且对眼睛不自然的经典校准方法。这种隐式校准需要一些关于用户正在观看的刺激的知识，并考虑这些信息来预测可能的注视目标。本文描述了一种执行隐式校准的可能方法：从寻找可能的注视目标 (PFT) 开始，然后使用这些目标构建映射-可能的注视路径。本文介绍了可用于寻找 PFT 和映射的各种算法，并使用两个不同类型的眼动仪注册的两个数据集来计算误差。结果表明，尽管目前隐式校准的结果不如经典校准的结果好，但它可能与之相当，并且足以满足某些应用的需求。
