Computer Science > Computer Vision and Pattern Recognition
arXiv:2401.15996 (cs)
[Submitted on 29 Jan 2024 (v1), last revised 23 Feb 2024 (this version, v2)]
Title:AccessLens: Auto-detecting Inaccessibility of Everyday Objects
View a PDF of the paper titled AccessLens: Auto-detecting Inaccessibility of Everyday Objects, by Nahyun Kwon and 6 other authors
View PDFHTML (experimental)Abstract:In our increasingly diverse society, everyday physical interfaces often present barriers, impacting individuals across various contexts. This oversight, from small cabinet knobs to identical wall switches that can pose different contextual challenges, highlights an imperative need for solutions. Leveraging low-cost 3D-printed augmentations such as knob magnifiers and tactile labels seems promising, yet the process of discovering unrecognized barriers remains challenging because disability is context-dependent. We introduce AccessLens, an end-to-end system designed to identify inaccessible interfaces in daily objects, and recommend 3D-printable augmentations for accessibility enhancement. Our approach involves training a detector using the novel AccessDB dataset designed to automatically recognize 21 distinct Inaccessibility Classes (e.g., bar-small and round-rotate) within 6 common object categories (e.g., handle and knob). AccessMeta serves as a robust way to build a comprehensive dictionary linking these accessibility classes to open-source 3D augmentation designs. Experiments demonstrate our detector's performance in detecting inaccessible objects.
| Comments: | CHI2024 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2401.15996 [cs.CV] |
| (orarXiv:2401.15996v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2401.15996 arXiv-issued DOI via DataCite | |
| Related DOI: | https://doi.org/10.1145/3613904.3642767 DOI(s) linking to related resources |
Submission history
From: Nahyun Kwon [view email][v1] Mon, 29 Jan 2024 09:27:55 UTC (27,924 KB)
[v2] Fri, 23 Feb 2024 17:06:14 UTC (12,262 KB)
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View a PDF of the paper titled AccessLens: Auto-detecting Inaccessibility of Everyday Objects, by Nahyun Kwon and 6 other authors
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