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 ofzebra crosswalksin 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.
authors.affiliation: Università degli Studi di Milano.
authors.forename: Dragan
authors.initials: D
authors.lastname: Ahmetovic
authors.affiliation: University of California Santa Cruz.
authors.forename: Roberto
authors.initials: R
authors.lastname: Manduchi
authors.affiliation: Smith-Kettlewell Eye Research Institute.
authors.forename: James M
authors.initials: JM
authors.lastname: Coughlan
authors.affiliation: Università degli Studi di Milano.
authors.forename: Sergio
authors.initials: S
authors.lastname: Mascetti
references.citation: J Assist Technol. 2013 Apr 1;7(2):null
references.pmid: 24353745
references.citation: IEEE Int Conf Multimed Expo Workshops. 2013 Jul;2013:1-7
references.pmid: 25360440