IEEE Trans Vis Comput Graph. 2024 Nov;30(11):7277-7287. doi: 10.1109/TVCG.2024.3456164. Epub 2024 Oct 10.
With eye tracking finding widespread utility in augmented reality and virtual reality headsets, eye gaze has the potential to recognize users' visual tasks and adaptively adjust virtual content displays, thereby enhancing the intelligence of these headsets. However, current studies on visual task recognition often focus on scene-specific tasks, like copying tasks for office environments, which lack applicability to new scenarios, e.g., museums. In this paper, we propose four scene-agnostic task types for facilitating task type recognition across a broader range of scenarios. We present a new dataset that includes eye and head movement data recorded from 20 participants while they engaged in four task types across 15 360-degree VR videos. Using this dataset, we propose an egocentric gaze-aware task type recognition method, TRCLP, which achieves promising results. Additionally, we illustrate the practical applications of task type recognition with three examples. Our work offers valuable insights for content developers in designing task-aware intelligent applications. Our dataset and source code are available at zhimin-wang.github.io/TaskTypeRecognition.html.
眼动追踪在增强现实和虚拟现实耳机中得到了广泛应用，眼注视具有识别用户视觉任务并自适应调整虚拟内容显示的潜力，从而提高这些耳机的智能性。然而，当前关于视觉任务识别的研究通常侧重于特定场景的任务，例如办公环境中的复制任务，这些任务缺乏对新场景的适用性，例如博物馆。在本文中，我们提出了四种与场景无关的任务类型，以促进更广泛场景下的任务类型识别。我们提出了一个新的数据集，其中包括 20 名参与者在 15 个 360 度 VR 视频中进行四种任务类型时记录的眼动和头部运动数据。使用这个数据集，我们提出了一种基于自我中心注视的任务类型识别方法 TRCLP，该方法取得了有前景的结果。此外，我们通过三个示例说明了任务类型识别的实际应用。我们的工作为内容开发者设计任务感知智能应用提供了有价值的见解。我们的数据集和源代码可在 zhimin-wang.github.io/TaskTypeRecognition.html 上获取。
