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首页> 外文会议>2018 17th International Symposium INFOTEH-JAHORINA >Deep neural network ensemble architecture for eye movements classification
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Deep neural network ensemble architecture for eye movements classification

机译：用于眼睛运动分类的深度神经网络集成架构

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摘要

Up to now, eye tracking technologies have been used for different purposes in various industries, from medical to gaming. Eye tracking methods could include predicting fixations, gaze mapping or movement classification. Recent advances in deep learning techniques provide possibilities for solving many computer vision tasks with high accuracy. Authors of this paper propose a novel deep learning based architecture for eye movement classification task. Proposed architecture is an ensemble approach which employs deep convolutional neural networks that run in parallel, for both eyes separately, for visual feature extractions along with recurrent layers for temporal information gathering. Dataset images for training and validation were gathered from standard web camera and pre-processed automatically using dedicated tools. Overall accuracy of developed classifier on the validation set was 92%. Proposed architecture uses relatively small networks which brings the possibility of real time usage (successfully tested on 15-20fps) on regular CPU. Classifier achieved overall accuracy of 88% on the real-time test, using standard laptop and web camera.
机译：迄今为止，从医疗到游戏，眼动追踪技术已在各种行业中用于不同目的。眼睛跟踪方法可以包括预测注视，注视映射或运动分类。深度学习技术的最新进展为高精度解决许多计算机视觉任务提供了可能性。本文的作者提出了一种新颖的基于深度学习的眼动分类任务架构。拟议的体系结构是一种集成方法，该方法采用并行运行的深层卷积神经网络（对于两只眼睛分别），以进行视觉特征提取，同时使用递归层进行时域信息收集。用于训练和验证的数据集图像是从标准网络摄像头收集的，并使用专用工具自动进行了预处理。验证集上开发的分类器的总体准确性为92％。提议的体系结构使用相对较小的网络，这带来了在常规CPU上实时使用（以15-20fps成功测试）的可能性。使用标准笔记本电脑和网络摄像头，分类器在实时测试中的整体准确性达到88％。

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