发表机构
University of Wisconsin-Madison; University of Washington; The University of Texas at Dallas（威斯康星大学麦迪逊分校; 华盛顿大学; 德克萨斯大学达拉斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对低视力人群在复杂场景中难以感知多物体的问题，构建可穿戴AR系统SceneGlance按重要性区分物体，经实验发现能转移其注意力并支持相关感知策略，但存在权衡，还揭示了复杂场景中AR增强的挑战及设计启示。
AI 中文摘要
低视力人群难以感知诸如繁忙厨房和拥挤街道等包含众多物体、视觉杂乱和动态元素的复杂场景。先前针对低视力的AR系统要么增强低级视觉特征，要么在简单设置中为单一任务增强与任务相关的物体，复杂场景中的多物体增强未得到充分探索。基于一项定性研究，我们构建了可穿戴AR系统SceneGlance，它能识别重要物体并按重要性级别进行视觉区分。通过在模拟厨房场景对12名低视力人群进行的对照实验室研究以及在户外路线对13名低视力人群进行的自由形式出声思考研究，发现物体重要性的AR区分将低视力人群的注意力转向更重要的物体，并支持诸如根据增强分布构建心理快照和按重要性进行分层扫描等感知策略。然而，这种注意力转移存在权衡，因为增强许多物体降低了整体场景召回率。研究还揭示了复杂场景中AR增强带来的挑战，如相邻增强相互融合或干扰，这为复杂现实世界中更实用的AR视觉增强系统带来了设计启示。
英文摘要
People with low vision (PLV) struggle to perceive complex scenes like busy kitchens and crowded streets, which contain many objects, visual clutter, and dynamic elements. Prior AR systems for low vision either enhance low-level visual features or augment task-relevant objects for single tasks in simple settings, leaving multi-object augmentation in complex scenes underexplored. Informed by a formative study characterizing important objects and their perceived importance for PLV, we built SceneGlance, a wearable AR system that recognizes important objects and visually distinguishes them by importance level. Through a controlled lab study with 12 PLV in a mock-up kitchen scene and a free-form think-aloud study with 13 PLV navigating an outdoor route, we found that AR distinction on object importance shifted PLV's attention toward objects of higher importance, and supported perception strategies such as building mental snapshots from the augmentation distribution and hierarchical scanning by importance. However, this attention shift came with a tradeoff of reduced overall scene recall. The studies also surfaced challenges posed by AR augmentations in complex scenes, such as adjacent augmentations blending or interfering with each other, yielding design implications for more practical AR vision enhancement systems in the complex real world.
Comments24 pages, 11 figures. Accepted to ASSETS '26 (28th International ACM SIGACCESS Conference on Computers and Accessibility)
