Urban intersections are the most dangerous parts of a blind person's travel. They are becoming increasingly complex, making safe crossing using conventional blind orientation and mobility techniques ever more difficult. To alleviate this problem, we propose to develop and evaluate a cell phone-based system to analyze images of street intersections, taken by a blind or visually impaired person using a standard camera cell phone, to provide real-time feedback. Drawing on our recent work on computer vision algorithms that help a blind person find crosswalks and other important features in a street intersection, as well as our ongoing work on cell phone implementations of algorithms for indoor wayfinding and for reading digital appliance displays, we will refine these algorithms and implement them on a cell phone. The information extracted by the algorithms will be communicated to the user with a combination of synthesized speech, audio tones and/or tactile feedback (using the cell phone's built-in vibrator). Human factors studies will help determine how to configure the system and its user controls for maximum effectiveness and ease of use, and provide an evaluation of the overall system. The street intersection analysis software will be made freely available for download into any camera-equipped cell phone that uses the widespread Symbian operating system (such as the popular Nokia cell phone series). The cell phone will not need any hardware modifications or add-ons to run this software. Ultimately a user will be able to download an entire suite of such algorithms for free onto the cell phone he or she is already likely to be carrying, without having to carry a separate piece of equipment for each function. Relevance: The ability to walk safely and confidently along sidewalks and traverse crosswalks is taken for granted every day by the sighted, but approximately 10 million Americans with significant vision impairments and a million who are legally blind face severe difficulties in this task. The proposed research would result in a highly accessible system (with zero or minimal cost to users) to augment existing wayfinding techniques, which could dramatically improve independent travel for blind and visually impaired persons.
| Ahmetovic, Dragan; Manduchi, Roberto; Coughlan, James M et al. (2017) Mind your crossings: Mining GIS imagery for crosswalk localization. ACM Trans Access Comput 9: |
| Ahmetovic, Dragan; Manduchi, Roberto; Coughlan, James M et al. (2015) Zebra Crossing Spotter: Automatic Population of Spatial Databases for Increased Safety of Blind Travelers. ASSETS 2015:251-258 |
| Fusco, Giovanni; Shen, Huiying; Murali, Vidya et al. (2014) Determining a Blind Pedestrian's Location and Orientation at Traffic Intersections. Comput Help People Spec Needs 8547:427-432 |
| Fusco, Giovanni; Shen, Huiying; Coughlan, James M (2014) Self-Localization at Street Intersections. Proc Conf Comput Robot Vis :40-47 |
| Murali, Vidya N; Coughlan, James M (2013) SMARTPHONE-BASED CROSSWALK DETECTION AND LOCALIZATION FOR VISUALLY IMPAIRED PEDESTRIANS. IEEE Int Conf Multimed Expo Workshops 2013:1-7 |
| Coughlan, James M; Shen, Huiying (2013) Crosswatch: a System for Providing Guidance to Visually Impaired Travelers at Traffic Intersections. J Assist Technol 7: |
| Manduchi, Roberto; Coughlan, James (2012) (Computer) Vision without Sight. Commun ACM 55:96-104 |
| Ivanchenko, Volodymyr; Coughlan, James; Shen, Huiying (2010) Real-Time Walk Light Detection with a Mobile Phone. Comput Help People Spec Needs 6180:229-234 |
| Ivanchenko, Volodymyr; Coughlan, James; Shen, Huiying (2010) Real-Time Walk Light Detection with a Mobile Phone. Lect Notes Comput Sci 6180:229-234 |
| Shen, Huiying; Coughlan, James; Ivanchenko, Volodymyr (2009) Figure-Ground Segmentation Using Factor Graphs. Image Vis Comput 27:854-863 |
Showing the most recent 10 out of 13 publications
