Gaze3D:用于三维重构场景凝视分析的框架

Thomas Booth, S. Sridharan, Vasudev Bethamcherla, Reynold J. Bailey
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引用次数: 4

摘要

头戴式眼动追踪器面临的一个持续挑战是如何分析来自多个观看同一场景的人的数据。我们的工作重点是静态场景。以前的方法包括捕捉场景的高分辨率全景,然后将所有观众的注视映射到这个全景上。然而,这种方法是有限的,因为它们通常限制所有观众从相同的静止有利位置观察场景。我们提出了一个将用户视角凝视数据与场景三维重建相结合的系统。该系统可以将多个观众的注视数据可视化到一个场景的3D模型上,而不是多个2D全景图。受试者可以自由地在他们认为合适的场景中移动,从而更自然地完成任务。此外,由于没有必要将场景摄像机视频扭曲成平面全景,我们的系统在可视化过程中保留了场景中物体的相对位置。这可以更好地了解查看者的问题解决和搜索任务策略。该系统在复杂的静态环境中具有很高的适用性,如犯罪现场和零售商店的营销研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gaze3D: framework for gaze analysis on 3D reconstructed scenes
An ongoing challenge with head-mounted eye-trackers is how to analyze the data from multiple individuals looking at the same scene. Our work focuses on static scenes. Previous approaches involve capturing a high resolution panorama of the scene and then mapping the fixations from all viewers onto this panorama. However such approaches are limited as they typically restrict all viewers to observe the scene from the same stationary vantage point. We present a system which incorporates user-perspective gaze data with a 3D reconstruction of the scene. The system enables the visualization of gaze data from multiple viewers on a single 3D model of the scene instead of multiple 2D panoramas. The subjects are free to move about the scene as they see fit which leads to more natural task performance. Furthermore since it is not necessary to warp the scene camera video into a flat panorama, our system preserves the relative positions of the objects in the scene during the visualization process. This gives better insight into the viewer's problem solving and search task strategies. Our system has high applicability in complex static environments such as crime scenes and marketing studies in retail stores.
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