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

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.

Authors

Institutions

Publication Details

Published
2024-10-11
DOI
https://doi.org/10.1145/3654777.3676333
Citations
11
Primary Topic
Tactile and Sensory Interactions
Type
article
Field-Weighted Citation Impact
3.77
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Zoran Kostić, Xin Yi Therese Xu, Mahshid Ghasemi, Michael Malcolm et al.
11 citations
Tactile and Sensory Interactions
3.77
article

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

Zoran Kostić, Xin Yi Therese Xu, Mahshid Ghasemi, Michael Malcolm, Mehmet Kerem Türkcan, Sophie Ana Paris, Gil Zussman, Gaurav Jain, Javad Ghaderi, Zihao Zhang, Basel Hindi, Brian A. Smith, Koushik Srinivasula, Mingyu Xie, Daniel Weiner
article en
11 citations

Abstract

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.

Lehman College (US), Pomona College (US), New York University (US), New York City College of Technology (US), Columbia University (US)
Sustainable cities and communities
Openalex Percentile: Top 6%
Tactile and Sensory Interactions
3.77
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.