Mind Your Crossings

Author:

Ahmetovic Dragan1ORCID,Manduchi Roberto2,Coughlan James M.3,Mascetti Sergio4

Affiliation:

1. Carnegie Mellon University, Pittsburgh, PA

2. University of California Santa Cruz, Santa Cruz, CA

3. The Smith-Kettlewell Eye Research Institute, San Francisco, CA

4. Università degli Studi di Milano

Abstract

For blind travelers, finding crosswalks and remaining within their borders while traversing them is a crucial part of any trip involving street crossings. While standard Orientation & Mobility (O&M) techniques allow blind travelers to safely negotiate street crossings, additional information about crosswalks and other important features at intersections would be helpful in many situations, resulting in greater safety and/or comfort during independent travel. For instance, in planning a trip a blind pedestrian may wish to be informed of the presence of all marked crossings near a desired route. We have conducted a survey of several O&M experts from the United States and Italy to determine the role that crosswalks play in travel by blind pedestrians. The results show stark differences between survey respondents from the U.S. compared with Italy: the former group emphasized the importance of following standard O&M techniques at all legal crossings (marked or unmarked), while the latter group strongly recommended crossing at marked crossings whenever possible. These contrasting opinions reflect differences in the traffic regulations of the two countries and highlight the diversity of needs that travelers in different regions may have. To address the challenges faced by blind pedestrians in negotiating street crossings, we devised a computer vision--based technique that mines existing spatial image databases for discovery of zebra crosswalks in urban settings. Our algorithm first searches for zebra crosswalks in satellite images; all candidates thus found are validated against spatially registered Google Street View images. This cascaded approach enables fast and reliable discovery and localization of zebra crosswalks in large image datasets. While fully automatic, our algorithm can be improved by a final crowdsourcing validation. To this end, we developed a Pedestrian Crossing Human Validation web service, which supports crowdsourcing, to rule out false positives and identify false negatives.

Funder

“Fondo Supporto alla Ricerca 2015”

“Assistive Technologies on Mobile Devices”

Independent Living and Rehabilitation Research

National Institutes of Health

Administration for Community Living's National Institute on Disability

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Science Applications,Human-Computer Interaction

Cited by 36 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Human–AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era;Future Internet;2024-07-18

2. Marked crosswalks, station area built environments, and transit ridership: Associations between changes in 877 US TOD stations, 2010–2018;Transportation Research Part A: Policy and Practice;2024-01

3. Automatically detect crosswalks from satellite view images – A deep learning approach with ground truth verification;International Journal of Transportation Science and Technology;2024-01

4. Crosswalk Detection from Satellite Imagery for Pedestrian Network Completion;Transportation Research Record: Journal of the Transportation Research Board;2023-11-27

5. Towards Street Camera-based Outdoor Navigation for Blind Pedestrians;The 25th International ACM SIGACCESS Conference on Computers and Accessibility;2023-10-22

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