论文标题
分层的分类分类
Hierarchical HMM for Eye Movement Classification
论文作者
论文摘要
在这项工作中，我们解决了三元眼动分类的问题，该分类旨在将固定，扫视和平滑的追求与原始眼睛位置数据分开。这些不同类型的眼动的有效分类有助于更好地分析和利用眼睛跟踪数据。与现有的方法通过几个预定义的阈值值检测眼动的方法不同，我们提出了一种用于检测固定固定，扫视和平滑追求的分层隐藏模型（HMM）统计算法。所提出的算法利用了通过分层分类策略的记录的原始眼睛跟踪数据的不同特征，每次将一种类型的眼动分开。实验结果证明了与最新方法相比，通过实现竞争性或更好的性能来证明该方法的有效性和鲁棒性。
In this work, we tackle the problem of ternary eye movement classification, which aims to separate fixations, saccades and smooth pursuits from the raw eye positional data. The efficient classification of these different types of eye movements helps to better analyze and utilize the eye tracking data. Different from the existing methods that detect eye movement by several pre-defined threshold values, we propose a hierarchical Hidden Markov Model (HMM) statistical algorithm for detecting fixations, saccades and smooth pursuits. The proposed algorithm leverages different features from the recorded raw eye tracking data with a hierarchical classification strategy, separating one type of eye movement each time. Experimental results demonstrate the effectiveness and robustness of the proposed method by achieving competitive or better performance compared to the state-of-the-art methods.
