A Cell Phone-Based Street Intersection Analyzer for Visually Impaired Pedestrians Abstract Project Summary Urban intersections are among 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 cell phone, to provide real-time feedback. Building on our past work on a prototype """"""""Crosswatch"""""""" system that uses computer vision algorithms to find crosswalks and Walk lights, we will greatly enhance the functionality of the system with information about the intersection layout and the identity of its connecting streets, the presence of stop signs, one-way signs and other controls indicating right-of-way, and timing information integrated from Walk/Don't Walk lights, countdown timers and other traffic lights. The system will convey intersection information, and will actively guide the user to align himself/herself with crosswalks, using a combination of synthesized speech and audio tones. We will conduct human factors studies to optimize the system functionality and the configuration of the user interface, as well as develop interactive training applications to equip users with the skills to better use the system. These training applications, implemented as additional cell phone software to complement the intersection system, will train users to hold the camera horizontal and forward and to minimize veer when traversing a crosswalk. The intersection analysis and training software will be made freely available for download onto popular cell phones (such as iPhone, Android or Symbian models). 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.

Public Health 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.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
5R01EY018345-06
Application #
8435501
Study Section
Special Emphasis Panel (ZRG1-ETTN-E (92))
Program Officer
Wiggs, Cheri
Project Start
2007-09-01
Project End
2015-02-28
Budget Start
2013-03-01
Budget End
2015-02-28
Support Year
6
Fiscal Year
2013
Total Cost
$383,018
Indirect Cost
$145,518
Name
Smith-Kettlewell Eye Research Institute
Department
Type
DUNS #
073121105
City
San Francisco
State
CA
Country
United States
Zip Code
94115
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; Coughlan, James M (2014) Self-Localization at Street Intersections. Proc Conf Comput Robot Vis :40-47
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
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
Ivanchenko, V; Coughlan, J; Shen, H (2009) Staying in the Crosswalk: A System for Guiding Visually Impaired Pedestrians at Traffic Intersections. Assist technol Res Ser 25:69-73

Showing the most recent 10 out of 12 publications