An Initial Study of Automatic Curb Ramp Detection with Crowdsourced Verification Using Google Street View Images

Authors

  • Kotaro Hara University of Maryland, College Park
  • Jin Sun University of Maryland, College Park
  • Jonah Chazan Montgomery Blair High School
  • David Jacobs University of Maryland, College Park
  • Jon Froehlich University of Maryland, College Park

DOI:

https://doi.org/10.1609/hcomp.v1i1.13109

Keywords:

Crowdsourcing accessibility, accessible urban navigation, Google Street View, Amazon Mechanical Turk

Abstract

In our previous research, we examined whether minimally trained crowd workers could find, categorize, and assess sidewalk accessibility problems using Google Street View (GSV) images. This poster paper presents a first step towards combining automated methods (e.g., machine vision-based curb ramp detectors) in concert with human computation to improve the overall scalability of our approach.

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Published

2013-11-03

How to Cite

Hara, K., Sun, J., Chazan, J., Jacobs, D., & Froehlich, J. (2013). An Initial Study of Automatic Curb Ramp Detection with Crowdsourced Verification Using Google Street View Images. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 1(1), 32-33. https://doi.org/10.1609/hcomp.v1i1.13109

Issue

Vol. 1 (2013): First AAAI Conference on Human Computation and Crowdsourcing

Section

Works in Progress