Automated Scene Understanding

Award Information
Agency:
Department of Defense
Branch
Office of the Secretary of Defense
Amount:
$99,918.00
Award Year:
2009
Program:
SBIR
Phase:
Phase I
Contract:
N00014-10-M-0082
Award Id:
91423
Agency Tracking Number:
O092-SP3-4005
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
11600 Sunrise Valley Drive, Suite # 290, Reston, VA, 20191
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
038732173
Principal Investigator:
Mun Wai Lee
Principal Investigator
(703) 654-9300
mlee@objectvideo.com
Business Contact:
Paul Brewer
VP, New Technology
(703) 654-9314
pbrewer@objectvideo.com
Research Institution:
n/a
Abstract
Automatic visual content extraction and scene understanding is an enabling technology for video surveillance, situational awareness, and force protection applications. High-level scene comprehension requires a deep understanding of objects, scene elements and their inter-relations. Current systems lack a general visual knowledge framework and efficient computational algorithms for detecting large number of object categories. We propose to develop video inference algorithms and a modular architecture for scene understanding based on the computational framework of And-Or graph. Various image inference modules can be easily integrated to the framework through an API for scene understanding. The architecture should also support the input of user-provided context to improve inference. Leveraging earlier work on semantic annotation, we will develop algorithms to infer complex relationships between scene entities. Plain text reports of the scene will be automatically generated to describe these relationships, contextual information, as well as events of interest. To achieve high compression rate for bandwidth-constraint applications, the text description are used to synthesize image and video to provide a representative rendering of the scene and events. In addition, we propose a method for indexing hierarchical data as well as a scalable framework for searching large imagery dataset.

* information listed above is at the time of submission.

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