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Identifying Crucial Objects in Blind and Low-Vision Individuals' Navigation
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This paper presents a curated list of 90 objects essential for the navigation of blind and low-vision (BLV) individuals, encompassing road, sidewalk, and indoor environments. We develop the initial list by analyzing 21 publicly available videos featuring BLV individuals navigating various settings. Then, we refine the list through feedback from a focus group study involving blind, low-vision, and sighted companions of BLV individuals. A subsequent analysis reveals that most contemporary datasets used to train recent computer vision models contain only a small subset of the objects in our proposed list. Furthermore, we provide detailed object labeling for these 90 objects across 31 video segments derived from the original 21 videos. Finally, we make the object list, the 21 videos, and object labeling in the 31 video segments publicly available. This paper aims to fill the existing gap and foster the development of more inclusive and effective navigation aids for the BLV community.
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Cited by 1 Pith paper
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WalkVLM:Aid Visually Impaired People Walking by Vision Language Model
WalkVLM combines chain-of-thought reasoning with a temporal trigger module, and the new Walking Awareness Dataset provides 12,000 annotated walking videos to benchmark AI walking assistance.
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