Activity recognition using Video Event Segmentation with Text (VEST)

Hillary Holloway; Eric K. Jones; Andrew Kaluzniacki; Erik Blasch; Jorge Tierno

doi:10.1117/12.2050413

20 June 2014 Activity recognition using Video Event Segmentation with Text (VEST)

Hillary Holloway, Eric K. Jones, Andrew Kaluzniacki, Erik Blasch, Jorge Tierno

Proceedings Volume 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII; 90910O (2014) https://doi.org/10.1117/12.2050413
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

Multi-Intelligence (multi-INT) data includes video, text, and signals that require analysis by operators. Analysis methods include information fusion approaches such as filtering, correlation, and association. In this paper, we discuss the Video Event Segmentation with Text (VEST) method, which provides event boundaries of an activity to compile related message and video clips for future interest. VEST infers meaningful activities by clustering multiple streams of time-sequenced multi-INT intelligence data and derived fusion products. We discuss exemplar results that segment raw full-motion video (FMV) data by using extracted commentary message timestamps, FMV metadata, and user-defined queries.

Citation Download Citation

Hillary Holloway, Eric K. Jones, Andrew Kaluzniacki, Erik Blasch, and Jorge Tierno "Activity recognition using Video Event Segmentation with Text (VEST)", Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 90910O (20 June 2014); https://doi.org/10.1117/12.2050413

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