HCC: Medium: Combining Crowdsourcing and Computer Vision for Street-level Accessibility

尽管为美国残疾人制定了全面的民权立法，但许多城市街道，人行道和企业仍然无法进入。 问题不仅在于街道一级的无障碍环境影响到人们在城市中的出行地点和方式，而且还在于几乎没有任何机制可以事先确定城市的无障碍区域。 传统上，人行道评估是通过亲自进行街道审计来进行的，这是劳动密集型和昂贵的，或者通过公民来电报告来进行，这是在反应的基础上进行的。 虽然人们努力将城市中的步行能力、自行车能力和公共交通的可用性可视化，但没有类似的无障碍努力。 因此，例如，轮椅使用者经常避免去他们不知道无障碍路线的城市新地区。 PI计划通过双管齐下的方法来解决这个问题，首先，他将开发可扩展的数据收集方法，用于使用众包，计算机视觉和在线地图图像的组合来获取人行道无障碍信息;然后，他将使用新数据来开发和评估一套新颖的导航和地图工具。 为此，首席研究员及其团队将收集和分析来自行动不便人士和ADA街景设计专家的访谈和调查数据，并将设法了解行动不便人士如何利用互动地图信息来提高行动能力。 他们将研究高效和有效的众包地图标记任务的方法，经验性地评估现有方法，并设计新颖，更有效的方法。 他们将开发新的计算机视觉算法来分析街道场景，通过将人类标记工作集中在最有可能包含重大问题的位置上，来帮助扩展数据收集。 他们将设计、实施和评估新的基于地图的无障碍工具，以帮助有行动障碍的人在城市中导航。 根据研究的每个阶段，用户评估将包括实验室和实地研究。更广泛的影响：在美国，大约有3060万人有身体残疾，影响他们的走动活动，近一半的人报告使用辅助工具，如轮椅，手杖，拐杖，或步行者。 这项研究的结果将对这些美国人独立旅行的能力产生重大影响，通过改变美国每条人行道，街道和建筑立面的无障碍信息的收集和可视化方式。 项目成果将包括一个可供公众访问的网站，在该网站上，将提供在这项工作中收集的标记数据和开发的新原型工具，供一般使用。 此外，PI和Co-PI将在整个项目过程中为研究生和本科生提供建议和指导，其中包括两名博士和两名MS学生，他们将获得人机交互和计算机视觉的跨学科教育。

Despite comprehensive civil rights legislation for Americans with disabilities, 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. Traditionally, sidewalk assessment has been conducted via in-person street audits, which are labor intensive and costly, or via citizen call-in reports, which are done on a reactive basis. And while efforts exist for visualizing the walk-ability, bike-ability, and availability of public transport in cities, there are no analogous efforts for accessibility. Thus, wheelchair users, for example, often avoid going to new areas of a city where they don't know about accessible routes. The PI plans to address this problem by means of a two-pronged approach in which he will first develop scalable data collection methods for acquiring sidewalk accessibility information using a combination of crowd-sourcing, computer vision, and online map imagery; he will then use the new data to develop and evaluate a novel set of navigation and map tools for accessibility. To these ends, the PI and his team will collect and analyze interview and survey data both from mobility impaired persons and from ADA streetscape design experts, and will seek to understand how people with mobility impairments can make use of interactive mapping information to enhance mobility. They will study methods for efficiently and effectively crowd-sourcing map labeling tasks, evaluating existing approaches empirically and designing novel, more effective approaches. They will develop new computer vision algorithms for the analysis of street scenes, which will be used to help scale the data collection by focusing human labeling efforts on locations that are most likely to contain significant problems. And they will design, implement and evaluate new accessible-aware map-based tools to aid people with mobility impairments in navigating their cities. As appropriate for each phase of the research, user evaluations will include both lab and field studies.Broader Impacts: Roughly 30.6 million individuals in the United States have physical disabilities that affect their ambulatory activities, and nearly half of these individuals report using an assistive aid such as a wheelchair, cane, crutches, or walker. The outcomes from this research will have a significant impact on the ability of these Americans to travel independently, by transforming the ways in which accessibility information is collected and visualized for every sidewalk, street, and building façade in America. Project outcomes will include a publicly accessible web site where both the labeled data collected during this work and the new prototype tools developed will be made available for general use. Furthermore, the PI and Co-PI will advise and mentor both graduate and undergraduate students throughout the course of the project, including two PhDs and two MS students who will obtain a cross-disciplinary education in human-computer interaction and computer vision.