探索眼动分类的简单神经网络架构

Jonas Goltz, M. Grossberg, Ronak Etemadpour
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引用次数: 3

摘要

人眼注视分析是研究人机交互和可视化的重要工具。然而,眼动追踪系统只能通过产生大量的坐标时间序列数据来报告现场的眼球注视。为了能够使用这些数据,我们必须首先提取显著事件,如眼球注视、扫视和后扫视振荡(PSO)。手动提取这些事件既耗时又费力,而且易变。在本文中,我们提出并评估了基于监督学习的简单快速的眼球注视分析自动解决方案。与最近的一些研究类似,我们开发了不同的简单神经网络,证明特征学习在从凝视坐标序列中识别事件方面产生了更好的结果。我们不应用任何特别的后处理,因此创建了一个完全自动化的端到端算法,其性能与当前最先进的架构一样好。经过训练后,它们的速度足够快,可以在近乎实时的环境中运行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring simple neural network architectures for eye movement classification
Analysis of eye-gaze is a critical tool for studying human-computer interaction and visualization. Yet eye tracking systems only report eye-gaze on the scene by producing large volumes of coordinate time series data. To be able to use this data, we must first extract salient events such as eye fixations, saccades, and post-saccadic oscillations (PSO). Manually extracting these events is time-consuming, labor-intensive and subject to variability. In this paper, we present and evaluate simple and fast automatic solutions for eye-gaze analysis based on supervised learning. Similar to some recent studies, we developed different simple neural networks demonstrating that feature learning produces superior results in identifying events from sequences of gaze coordinates. We do not apply any ad-hoc post-processing, thus creating a fully automated end-to-end algorithms that perform as good as current state-of-the-art architectures. Once trained they are fast enough to be run in a near real time setting.
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