StreetNav: Leveraging Street Cameras to Support Precise Outdoor Navigation for Blind Pedestrians

要旨

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.

著者
Gaurav Jain
Columbia University, New York, New York, United States
Basel Hindi
Columbia University , New York, New York, United States
Zihao Zhang
Columbia University, New York, New York, United States
Koushik Srinivasula
Columbia University , New York , New York, United States
Mingyu Xie
Columbia University, New York, New York, United States
Mahshid Ghasemi
Columbia University, New York, New York, United States
Daniel Weiner
Lehman College, Bronx, New York, United States
Sophie Ana Paris
New York University, New York, New York, United States
Xin Yi Therese Xu
Pomona College, Claremont, California, United States
Michael C. Malcolm
New York City College of Technology, Brooklyn , New York, United States
Mehmet Kerem Turkcan
Columbia University, New York, New York, United States
Javad Ghaderi
Columbia University, New York, New York, United States
Zoran Kostic
Columbia University, New York, New York, United States
Gil Zussman
Columbia University , New York, New York, United States
Brian A.. Smith
Columbia University, New York, New York, United States
論文URL

https://doi.org/10.1145/3654777.3676333

動画

会議: UIST 2024

ACM Symposium on User Interface Software and Technology

セッション: 2. Contextual Augmentations

Westin: Allegheny 2
4 件の発表
2024-10-17 00:35:00
2024-10-17 01:35:00