Paper Info
Title
Zebra Crossing Spotter: Automatic Population of Spatial Databases for Increased Safety of Blind Travelers.
Abstract
In this paper we propose a computer vision-based technique that mines existing spatial image databases for discovery of zebra crosswalks in urban settings. Knowing the location of crosswalks is critical for a blind person planning a trip that includes street crossing. By augmenting existing spatial databases (such as Google Maps or OpenStreetMap) with this information, a blind traveler may make more informed routing decisions, resulting in greater safety during independent travel. 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 could also be complemented by a final crowdsourcing validation stage for increased accuracy.
Year
DOI
Venue
2015
10.1145/2700648.2809847
ACM Conference on Supporting Group Work
Keywords
Field
DocType
Orientation and Mobility,Autonomous navigation,Visual impairments and blindness,Satellite and street-level imagery,Crowdsourcing
Data mining,Zebra crossing,Population,Computer science,Crowdsourcing,Orientation and Mobility,Database
Conference
Volume
Citations 
PageRank 
2015
12
0.84
References 
Authors
15
4
Name
Order
Citations
PageRank
Dragan Ahmetovic116621.09
Roberto Manduchi23528242.41
James Coughlan31133154.73
Sergio Mascetti449439.13