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This talk was given as part of the Human-Computer Interaction Institute seminar series at Carnegie Mellon University. My host was Professor Jeffrey Bigham. More info here: https://www.hcii.cmu.edu/news/seminar/event/2014/10/characterizing-physical-world-accessibility-scale-using-crowdsourcing-computer-vision-machine-learning
You can download the original PowerPoint deck with videos here:
Abstract: Roughly 30.6 million individuals in the US have physical disabilities that affect their ambulatory activities; nearly half of those individuals report using an assistive aid such as a wheelchair, cane, crutches, or walker. Despite comprehensive civil rights legislation, many city streets, sidewalks, and businesses remain inaccessible. The problem is not just that street-level accessibility affects where and how people travel in cities but also that there are few, if any, mechanisms to determine accessible areas of a city a priori.
In this talk, I will describe our research developing novel, scalable data-collection methods for acquiring accessibility information about the built environment using a combination of crowdsourcing, computer vision, and online map imagery (e.g., Google Street View). Our overarching goal is to transform the ways in which accessibility information is collected and visualized for every sidewalk, street, and building façade in the world. This work is in collaboration with University of Maryland Professor David Jacobs and graduate students Kotaro Hara and Jin Sun along with a number of undergraduate students and high school interns.