A unified framework for false alarm reduction using scene context from airborne sensors

Award Information
Agency:
Department of Defense
Branch:
Defense Advanced Research Projects Agency
Amount:
$1,249,460.00
Award Year:
2009
Program:
SBIR
Phase:
Phase II
Contract:
W31P4Q-09-C-0268
Agency Tracking Number:
07SB2-0308
Solicitation Year:
2007
Solicitation Topic Code:
SB072-017
Solicitation Number:
2007.2
Small Business Information
ObjectVideo
11600 Sunrise Valley Drive, Suite # 290, Reston, VA, 20191
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
038732173
Principal Investigator
 Khurram Hassan-Shafique
 Research Scientist
 (703) 654-9300
 kshafique@objectvideo.com
Business Contact
 Paul Brewer
Title: VP, New Technology
Phone: (703) 654-9314
Email: pbrewer@objectvideo.com
Research Institution
N/A
Abstract
This SBIR Phase II project will develop an integrated system that will extract scene and geometric context from aerial videos and utilize it for robust target detection and false alarm mitigation. The key innovation in this effort is to place localized target detection in the context of the overall 3D scene, its constituents, and activities by modeling the interdependence of targets, scene elements, scene and sensor’s geometry, and target-movement patterns. The Phase I of this effort demonstrated the proof of this concept by developing core technologies that included i) extraction of scene elements (such as, roads, vegetation, buildings) from the imagery, ii) parallax modeling, iii) extraction of geometric context (for example, parallax motion field and relative structure), iv) sensor modeling, and v) context based false alarm reduction. The Phase II effort will be focused towards refinement, advancement, and integration of these enabling technologies as well as handling operational issues that include efficient and effective processing of compressed videos, very high resolution imagery, and sensor metadata. In particular, the Phase II effort will include: implementation and refinement of enabling algorithms, development of an integrated system, software and algorithmic optimization, and detailed quantitative and qualitative evaluation of the system and its components.

* information listed above is at the time of submission.

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