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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.00491 |
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| _version_ | 1866910545825759232 |
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| author | Jain, Gaurav Hindi, Basel Zhang, Zihao Srinivasula, Koushik Xie, Mingyu Ghasemi, Mahshid Weiner, Daniel Paris, Sophie Ana Xu, Xin Yi Therese Malcolm, Michael Turkcan, Mehmet Ghaderi, Javad Kostic, Zoran Zussman, Gil Smith, Brian A. |
| author_facet | Jain, Gaurav Hindi, Basel Zhang, Zihao Srinivasula, Koushik Xie, Mingyu Ghasemi, Mahshid Weiner, Daniel Paris, Sophie Ana Xu, Xin Yi Therese Malcolm, Michael Turkcan, Mehmet Ghaderi, Javad Kostic, Zoran Zussman, Gil Smith, Brian A. |
| contents | Blind and low-vision (BLV) people rely on GPS-based systems for outdoor navigation. GPS's inaccuracy, however, causes them to veer off track, run into obstacles, and struggle to reach precise destinations. While prior work has made precise navigation possible indoors via hardware installations, enabling this outdoors remains a challenge. Interestingly, many outdoor environments are already instrumented with hardware such as street cameras. In this work, we explore the idea of repurposing existing street cameras for outdoor navigation. Our community-driven approach considers both technical and sociotechnical concerns through engagements with various stakeholders: BLV users, residents, business owners, and Community Board leadership. The resulting system, StreetNav, processes a camera's video feed using computer vision and gives BLV pedestrians real-time navigation assistance. Our evaluations show that StreetNav guides users more precisely than GPS, but its technical performance is sensitive to environmental occlusions and distance from the camera. We discuss future implications for deploying such systems at scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_00491 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | StreetNav: Leveraging Street Cameras to Support Precise Outdoor Navigation for Blind Pedestrians Jain, Gaurav Hindi, Basel Zhang, Zihao Srinivasula, Koushik Xie, Mingyu Ghasemi, Mahshid Weiner, Daniel Paris, Sophie Ana Xu, Xin Yi Therese Malcolm, Michael Turkcan, Mehmet Ghaderi, Javad Kostic, Zoran Zussman, Gil Smith, Brian A. Human-Computer Interaction Blind and low-vision (BLV) people rely on GPS-based systems for outdoor navigation. GPS's inaccuracy, however, causes them to veer off track, run into obstacles, and struggle to reach precise destinations. While prior work has made precise navigation possible indoors via hardware installations, enabling this outdoors remains a challenge. Interestingly, many outdoor environments are already instrumented with hardware such as street cameras. In this work, we explore the idea of repurposing existing street cameras for outdoor navigation. Our community-driven approach considers both technical and sociotechnical concerns through engagements with various stakeholders: BLV users, residents, business owners, and Community Board leadership. The resulting system, StreetNav, processes a camera's video feed using computer vision and gives BLV pedestrians real-time navigation assistance. Our evaluations show that StreetNav guides users more precisely than GPS, but its technical performance is sensitive to environmental occlusions and distance from the camera. We discuss future implications for deploying such systems at scale. |
| title | StreetNav: Leveraging Street Cameras to Support Precise Outdoor Navigation for Blind Pedestrians |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2310.00491 |