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Proceedings Paper

Advanced text and video analytics for proactive decision making
Author(s): Elizabeth K. Bowman; Matt Turek; Paul Tunison; Reed Porter; Steve Thomas; Vadas Gintautas; Peter Shargo; Jessica Lin; Qingzhe Li; Yifeng Gao; Xiaosheng Li; Ranjeev Mittu; Carolyn Penstein Rosé; Keith Maki; Chris Bogart; Samrihdi Shree Choudhari
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Paper Abstract

Today’s warfighters operate in a highly dynamic and uncertain world, and face many competing demands. Asymmetric warfare and the new focus on small, agile forces has altered the framework by which time critical information is digested and acted upon by decision makers. Finding and integrating decision-relevant information is increasingly difficult in data-dense environments. In this new information environment, agile data algorithms, machine learning software, and threat alert mechanisms must be developed to automatically create alerts and drive quick response. Yet these advanced technologies must be balanced with awareness of the underlying context to accurately interpret machine-processed indicators and warnings and recommendations. One promising approach to this challenge brings together information retrieval strategies from text, video, and imagery. In this paper, we describe a technology demonstration that represents two years of tri-service research seeking to meld text and video for enhanced content awareness. The demonstration used multisource data to find an intelligence solution to a problem using a common dataset. Three technology highlights from this effort include 1) Incorporation of external sources of context into imagery normalcy modeling and anomaly detection capabilities, 2) Automated discovery and monitoring of targeted users from social media text, regardless of language, and 3) The concurrent use of text and imagery to characterize behaviour using the concept of kinematic and text motifs to detect novel and anomalous patterns. Our demonstration provided a technology baseline for exploiting heterogeneous data sources to deliver timely and accurate synopses of data that contribute to a dynamic and comprehensive worldview.

Paper Details

Date Published: 3 May 2017
PDF: 20 pages
Proc. SPIE 10207, Next-Generation Analyst V, 102070K (3 May 2017); doi: 10.1117/12.2276369
Show Author Affiliations
Elizabeth K. Bowman, U.S. Army Research Lab. (United States)
Matt Turek, Kitware, Inc. (United States)
Paul Tunison, Kitware, Inc. (United States)
Reed Porter, Kitware, Inc. (United States)
Steve Thomas, Air Force Research Lab. (United States)
Vadas Gintautas, BAE Systems Electronic Systems (United States)
Peter Shargo, BAE Systems Electronic Systems (United States)
Jessica Lin, George Mason Univ. (United States)
Qingzhe Li, George Mason Univ. (United States)
Yifeng Gao, George Mason Univ. (United States)
Xiaosheng Li, George Mason Univ. (United States)
Ranjeev Mittu, U.S. Naval Research Lab. (United States)
Carolyn Penstein Rosé, Carnegie Mellon Univ. (United States)
Keith Maki, Carnegie Mellon Univ. (United States)
Chris Bogart, Carnegie Mellon Univ. (United States)
Samrihdi Shree Choudhari, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 10207:
Next-Generation Analyst V
Timothy P. Hanratty; James Llinas, Editor(s)

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