Noninvasive behavior observation techniques allow more natural human behavior assessment experiments with higher ecological validity. We propose the use of gaze ethograms in the context of user interaction with a computer display to characterize the user’s behavioral activity. A gaze ethogram is a time sequence of the screen regions the user is looking at. It can be used for the behavioral modeling of the user. Given a rough partition of the display space, we are able to extract gaze ethograms that allow discrimination of three common user behavioral activities: reading a text, viewing a video clip, and writing a text. A gaze tracking system is used to build the gaze ethogram. User behavioral activity is modeled by a classifier of gaze ethograms able to recognize the user activity after training. Conventional commercial gaze tracking for research in the neurosciences and psychology science are expensive and intrusive, sometimes impose wearing uncomfortable appliances. For the purposes of our behavioral research, we have developed an open source gaze tracking system that runs on conventional laptop computers using their low quality cameras. Some of the gaze tracking pipeline elements have been borrowed from the open source community. However, we have developed innovative solutions to some of the key issues that arise in the gaze tracker. Specifically, we have proposed texture-based eye features that are quite robust to low quality images. These features are the input for a classifier predicting the screen target area, the user is looking at. We report comparative results of several classifier architectures carried out in order to select the classifier to be used to extract the gaze ethograms for our behavioral research. We perform another classifier selection at the level of ethogram classification. Finally, we report encouraging results of user behavioral activity recognition experiments carried out over an inhouse dataset.

中文翻译：

---

基于注视特征图的行为活动识别

无创行为观察技术允许进行更自然的人类行为评估实验，具有更高的生态效度。我们建议在用户与计算机显示器交互的上下文中使用注视 ethograms 来表征用户的行为活动。凝视状态图是用户正在查看的屏幕区域的时间序列。它可用于用户的行为建模。给定显示空间的粗略划分，我们能够提取允许区分三种常见用户行为活动的注视 ethograms：阅读文本、查看视频剪辑和编写文本。凝视跟踪系统用于构建凝视行为图。用户行为活动由能够在训练后识别用户活动的注视 ethograms 分类器建模。用于神经科学和心理学研究的传统商业注视跟踪成本高昂且具有侵入性，有时需要佩戴不舒服的设备。出于我们行为研究的目的，我们开发了一个开源凝视跟踪系统，该系统使用低质量的摄像头在传统笔记本电脑上运行。一些注视跟踪管道元素是从开源社区借来的。然而，我们已经为凝视跟踪器中出现的一些关键问题开发了创新的解决方案。具体来说，我们提出了基于纹理的眼睛特征，这些特征对低质量图像非常稳健。这些特征是用于预测用户正在查看的屏幕目标区域的分类器的输入。我们报告了几种分类器体系结构的比较结果，以便选择分类器用于提取我们的行为研究的注视行为图。我们在 ethogram 分类级别执行另一个分类器选择。最后，我们报告了在内部数据集上进行的用户行为活动识别实验的令人鼓舞的结果。