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Computational Methods for Dynamic Scene Reconstruction

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-17-C-0532
Agency Tracking Number: N16A-017-0114
Amount: $993,630.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: N16A-T017
Solicitation Number: 2016.0
Solicitation Year: 2016
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-09-20
Award End Date (Contract End Date): 2019-12-04
Small Business Information
600 West Cummings Park
Woburn, MA 01801
United States
DUNS: 964928464
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Tom Pollard
 Sr. Member Technical Staff
 (781) 503-3287
Business Contact
 Melissa Joyce
Phone: (781) 305-4054
Research Institution
 University Of Maryland
 Takeia Bradley
Office of Research Administrat Lee Building
College Park, MD 20742
United States

 (301) 405-8061
 Nonprofit College or University

Mobile video sensors have become cheap and easy to integrate into cell-phones, UAVs, body cameras, and security cameras. The possibility of dense networks of video sensors enables a wide range of security-related uses for ONR and other government agencies including seaport monitoring, battlefield situational awareness, and forensic crime-scene analysis. The density and overlap of sensors creates the ability to keep track of all activity in a large area of interest, but also generates more data than a human monitor can process. Algorithms for automated detection and tracking of vehicles and people in video have advanced greatly in recent years, but are generally only usable on one video source at a time. Systems & Technology Research has teamed with the University of Maryland and Vision Systems Inc to develop a 4D semantic reconstruction framework that integrates geometric and human/vehicle detection information derived from multiple simultaneous video streams into a common time-varying 3D representation. This representation contains dense 3D geometry on static and moving objects as well as a high-level semantic understanding of the scene -- i.e., the position, orientation, and size of all moving objects.

* Information listed above is at the time of submission. *

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