Learning patterns of life from intelligence analyst chat

Michael K. Schneider; Mark Alford; Olga Babko-Malaya; Erik Blasch; Lingji Chen; Valentino Crespi; Jason HandUber; Phil Haney; Jim Nagy; Mike Richman; Gregory Von Pless; Howie Zhu; Bradley J. Rhodes

doi:10.1117/12.2225101

17 May 2016 Learning patterns of life from intelligence analyst chat

Michael K. Schneider, Mark Alford, Olga Babko-Malaya, Erik Blasch, Lingji Chen, Valentino Crespi, Jason HandUber, Phil Haney, Jim Nagy, Mike Richman, Gregory Von Pless, Howie Zhu, Bradley J. Rhodes

Author Affiliations +

Proceedings Volume 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV; 98420N (2016) https://doi.org/10.1117/12.2225101
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

Our Multi-INT Data Association Tool (MIDAT) learns patterns of life (POL) of a geographical area from video analyst observations called out in textual reporting. Typical approaches to learning POLs from video make use of computer vision algorithms to extract locations in space and time of various activities. Such approaches are subject to the detection and tracking performance of the video processing algorithms. Numerous examples of human analysts monitoring live video streams annotating or “calling out” relevant entities and activities exist, such as security analysis, crime-scene forensics, news reports, and sports commentary. This user description typically corresponds with textual capture, such as chat. Although the purpose of these text products is primarily to describe events as they happen, organizations typically archive the reports for extended periods. This archive provides a basis to build POLs. Such POLs are useful for diagnosis to assess activities in an area based on historical context, and for consumers of products, who gain an understanding of historical patterns. MIDAT combines natural language processing, multi-hypothesis tracking, and Multi-INT Activity Pattern Learning and Exploitation (MAPLE) technologies in an end-to-end lab prototype that processes textual products produced by video analysts, infers POLs, and highlights anomalies relative to those POLs with links to “tracks" of related activities performed by the same entity. MIDAT technologies perform well, achieving, for example, a 90% F1-value on extracting activities from the textual reports.

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Michael K. Schneider, Mark Alford, Olga Babko-Malaya, Erik Blasch, Lingji Chen, Valentino Crespi, Jason HandUber, Phil Haney, Jim Nagy, Mike Richman, Gregory Von Pless, Howie Zhu, and Bradley J. Rhodes "Learning patterns of life from intelligence analyst chat", Proc. SPIE 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV, 98420N (17 May 2016); https://doi.org/10.1117/12.2225101

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