Activity recognition using Video Event Segmentation with Text (VEST)
Hillary Holloway, Eric K. Jones, Andrew Kaluzniacki, Erik Blasch, Jorge E. Tierno · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
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.