Detecting eye movements in dynamic environments
Detecting eye movements in dynamic environments
复制标题
检测动态环境中的眼球运动
作者：
B. Reimer;M. Sodhi
为了利用越来越多的车载设备，汽车驾驶员必须在主要（驾驶）和次要（操作车载设备）任务之间分配注意力。然而，在诸如驾驶的动态环境中，识别和量化驾驶员如何专注于他/她同时从事的各种任务（包括分散注意力的任务）是不容易的。从驾驶员的扫描路径导出的测量值已被用作驾驶员注意力的相关性。本文提出了一种方法，用于分析眼睛的位置，这是一个主题的扫描路径的离散样本，以分类驱动器眼球运动。分析在动态环境中记录的眼睛位置的先前方法完全依赖于从叠加在记录场景的视频上的注视点手动识别视觉注意力的焦点，未能利用关于原始记录的眼睛位置中的运动结构的信息。虽然有效，但这些方法太耗时，当处理识别驾驶员之间的细微差异所需的大型数据集时，在不同的道路条件下，以及在不同的分心程度下，这些方法很难使用。本文提出的方法的目的是通过提出一种眼动分析方法来扩展眼动数据处理的自动化程度，该方法将自动化注视识别扩展到包括平滑和扫视运动。通过识别记录的眼睛位置中的眼睛运动，提出了一种将场景视频的分析减少到有限搜索空间的方法。描述了用于眼动分析的软件工具的实现，包括来自道路测试驾驶样本的示例。
To take advantage of the increasing number of in-vehicle devices, automobile drivers must divide their attention between primary (driving) and secondary (operating in-vehicle device) tasks. In dynamic environments such as driving, however, it is not easy to identify and quantify how a driver focuses on the various tasks he/she is simultaneously engaged in, including the distracting tasks. Measures derived from the driver’s scan path have been used as correlates of driver attention. This article presents a methodology for analyzing eye positions, which are discrete samples of a subject’s scan path, in order to categorize driver eye movements. Previous methods of analyzing eye positions recorded in a dynamic environment have relied completely on the manual identification of the focus of visual attention from a point of regard superimposed on a video of a recorded scene, failing to utilize information regarding movement structure in the raw recorded eye positions. Although effective, these methods are too time consuming to be easily used when the large data sets that would be required to identify subtle differences between drivers, under different road conditions, and with different levels of distraction are processed. The aim of the methods presented in this article are to extend the degree of automation in the processing of eye movement data by proposing a methodology for eye movement analysis that extends automated fixation identification to include smooth and saccadic movements. By identifying eye movements in the recorded eye positions, a method of reducing the analysis of scene video to a finite search space is presented. The implementation of a software tool for the eye movement analysis is described, including an example from an on-road test-driving sample.