ESARR: Enhanced Situational Awareness via Road Sign Recognition

Award Information
Agency: Department of Defense
Branch: Army
Contract: W56HZV-09-C-0039
Agency Tracking Number: A072-196-1569
Amount: $729,724.00
Phase: Phase II
Program: SBIR
Awards Year: 2009
Solicitation Year: 2007
Solicitation Topic Code: A07-196
Solicitation Number: 2007.2
Small Business Information
Quantum Signal, LLC
3741 Plaza Drive, Suite 1, Ann Arbor, MI, 48108
DUNS: 119094493
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Mitchell Rohde
 Chief Operating Officer
 (734) 994-0028
 rohde@quantumsignal.com
Business Contact
 Mitchell Rohde
Title: Chief Operating Officer
Phone: (734) 994-0028
Email: rohde@quantumsignal.com
Research Institution
N/A
Abstract
Situational Awareness (SA) systems leveraging Global Positioning Systems (GPS) and Geographical Information Systems (GIS) have become a mainstay for vehicles, troops, and even unmanned systems (such as unmanned ground vehicles (UGVs)). Unfortunately, in urban, semi-urban or canopied terrain GPS signals can be difficult or impossible to receive consistently. New and alternative tools to enhance situational awareness are needed to fill this gap. Road signs can potentially serve as an alternative to GPS in these environments – by definition; they are designed to provide information related to location. Quantum Signal LLC proposes to develop ESARR: a tool for enhanced situational awareness via road sign recognition. The system will be easy to operate, and will provide enhanced SA to the warfighter by: 1) Capturing & processing road sign images in stills/video & extracting information from them; 2) Relating the extracted information to maps/GIS to ascertain position (i.e. geo-bounding); 3) Efficiently communicating this information back to the soldier in a manner that is timely and informative. The system will be based on a combination of advanced image processing techniques and will be able to perform the road sign reading function robustly under a variety of conditions from video in real-time.

* information listed above is at the time of submission.

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