Automated Scene Understanding

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-10-M-0082
Agency Tracking Number: O092-SP3-4005
Amount: $99,918.00
Phase: Phase I
Program: SBIR
Awards Year: 2009
Solicitation Year: 2009
Solicitation Topic Code: OSD09-SP3
Solicitation Number: 2009.2
Small Business Information
11600 Sunrise Valley Drive, Suite # 290, Reston, VA, 20191
DUNS: 038732173
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Mun Wai Lee
 Principal Investigator
 (703) 654-9300
 mlee@objectvideo.com
Business Contact
 Paul Brewer
Title: VP, New Technology
Phone: (703) 654-9314
Email: 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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