| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | December 4, 2013 |
| Latest Amendment Date: | December 4, 2013 |
| Award Number: | 1345900 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Peter Atherton
patherto@nsf.gov (703)292-8772 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | January 1, 2014 |
| End Date: | October 31, 2014 (Estimated) |
| Total Intended Award Amount: | $150,000.00 |
| Total Awarded Amount to Date: | $150,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
313 2nd St SE Charlottesville VA US 22902-5686 (757)944-1132 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Charlottesville Virginia VA US 22902-5686 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | SBIR Phase I |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.084 |
ABSTRACT
This Small Business Innovation Research (SBIR) Phase I project will develop and commercialize a system for indexing, searching, and visualizing large unstructured collections of multimedia data (photos, videos, and audio recordings). In the aftermath of an unexpected disaster such as a terrorist attack, law enforcement is overwhelmed with a vast amount of multimedia assets from many different sources ranging from security cameras to witness smartphones. It is critically important for investigators to quickly and reliably identify potentially relevant footage in these large corpora in order to aid their investigation. This project will develop advanced software to address this need.
The broader impact/commercial potential of this project results from its applicability across a number of industries, including law enforcement, defense, manufacturing, and commercial retail. The combined size of these markets in the US is estimated to be roughly $6 trillion. This project addresses the need for automated tools to organize large unstructured collections of multimedia data that are expected to increase in number and size in parallel with the growth in smartphones, wider availability of low-cost recording equipment, and the prevalence of surveillance and security video systems. The current approach for searching these types of data collections will prove inadequate, since it relies on tools that are designed for reviewing a small amount of footage, normally captured by a small number of devices at known points in time and space.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
More of the world is recorded every second by private security cameras, traffic cameras, law enforcement surveillance systems, and millions of casual smartphone users. This observation especially holds true at large public gatherings like sporting events, political rallies, and concerts. When disasters occur like the recent terrorist attack at the Boston Marathon, big collections of multimedia data (photos, videos, and audio recordings) becomes evidence in a criminal investigation. Officials need tools that let them quickly identify footage within these large unstructured collections that show persons of interest and particular geographic locations (e.g., a street corner) at a specific time.
During our Phase I project, Arqball has developed a system for rapidly indexing, searching, and visualizing big collections of geotagged multimedia data. This system enables a fundamentally different and more effective approach to utilizing publicly available geotagged media such as Google Streetview, Instagram, and Flickr, as well as private collections of geotagged multimedia data commonly collected by surveillance systems, satellites, aircraft, automobiles, drones, and lapel or hand-held cameras. Analysts using this technology are able to search, filter, and explore based on geospatial, temporal, and related-text queries. Business opportunities for this technology exist in the law enforcement, defense and intelligence sectors, as well as large-scale manufacturing, retail, and other industries with large collections of photo or video data.
Last Modified: 12/30/2014
Modified by: Michael Holroyd
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