Pattern of Life Calculation from Big Graphs

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$79,990.00
Award Year:
2014
Program:
SBIR
Phase:
Phase I
Contract:
N00014-14-P-1155
Award Id:
n/a
Agency Tracking Number:
N141-075-0319
Solicitation Year:
2014
Solicitation Topic Code:
N141-075
Solicitation Number:
2014.1
Small Business Information
11600 Sunrise Valley Drive, Suite # 210, Reston, VA, 20191-
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
038732173
Principal Investigator:
NarayananRamanathan
Principal Investigator
(703) 654-9300
nramanathan@objectvideo.com
Business Contact:
GaryMyers
SVP, R&D
(703) 654-9359
gmyers@objectvideo.com
Research Institute:
n/a
Abstract
Pattern of Life characterization involves studying how people interact with the world around them on a daily basis: places they visit; people they interact with; actions they perform. Effective tracking of such patterns of life has direct implications to defense intelligence, cyber intelligence and corporate security. To solve this problem, ObjectVideo in collaboration with Dr. Leman Akoglu (Stony Brook), proposes to build a system that mines multiple data sources for relational information between people and places, detects anomalies and provides the tools to track the behaviors of anomalous entities. We will study patterns of life on four data domains: surveillance video, satellite imagery, text streams and non-traditional data sources such as weather feeds. We will use image / video analytics libraries to identify events from surveillance videos and detect change in satellite imagery. We will invoke Natural Language Processing tools to perform named entity recognition, entity resolution and relation estimation. Additionally, we will fit time-series models to weather feeds to study anomaly. We propose a unified graph representation to combine aforementioned data. Finally, we will invoke anomaly detection algorithms that scale up with large dynamically changing graphs and will build a visualization tool that helps analyst track anomalies.

* information listed above is at the time of submission.

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