Automated Scene Understanding

Award Information
Agency:
Department of Defense
Branch
Office of the Secretary of Defense
Amount:
$99,987.00
Award Year:
2010
Program:
SBIR
Phase:
Phase I
Contract:
N00014-11-M-0037
Award Id:
96920
Agency Tracking Number:
O102-L04-4067
Solicitation Year:
n/a
Solicitation Topic Code:
OSD 10-L04
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:
Atul Kanaujia
Principal Investigator
(703) 654-9300
akanaujia@objectvideo.com
Business Contact:
Paul Brewer
VP, New Technology
(703) 654-9314
pbrewer@objectvideo.com
Research Institution:
n/a
Abstract
Automatic extraction and representation of visual concepts and semantic information in scene is a desired capability in any security and surveillance operations. In the proposed effort we seek to advance the foundations of representation and fusion of data, information and knowledge gathered from diverse sources, and across multiple application domains. We target the problem of visual event recognition in network information environment, where faulty sensors, lack of effective visual processing tools and incomplete domain knowledge frequently cause uncertainty in the data set and consequently, in the visual primitives extracted from it. We adopt Markov Logic Network (MLN), that combines probabilistic graphical models and first order logic, to address the task of reasoning under uncertainty. MLN is a knowledge representation language that combines domain knowledge, visual concepts and experience to infer simple and complex real-world events. MLN generalizes over the existing state-of-the-art probabilistic models, including hidden Markov models, Bayesian networks, and stochastic grammars. Moreover, the framework can be made scalable to support variety of entities, activities and interactions that are typically observed in the real world. Experiments with real-world data and domain knowledge, in a variety of marine and urban settings, illustrate the mathematical soundness and wide-ranging applicability of our approach.

* information listed above is at the time of submission.

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