学术文献库

Organizations are starting to realize of the combined power of data and data-driven algorithmic models to gain insights, situational awareness, and advance their mission. A common challenge to gaining insights is connecting inherently different datasets. These datasets (e.g. geocoded features, video streams, raw text, social network data, etc.) per separate they provide very narrow answers; however collectively they can provide new capabilities. In this work, we present a data fusion framework for accelerating solutions for Processing, Exploitation, and Dissemination (PED). Our platform is a collection of services that extract information from several data sources (per separate) by leveraging deep learning and other means of processing. This information is fused by a set of analytical engines that perform data correlations, searches, and other modeling operations to combine information from the disparate data sources. As a result, events of interest are detected, geolocated, logged, and presented into a common operating picture. This common operating picture allows the user to visualize in real time all the data sources, per separate and their collective cooperation. In addition, forensic activities have been implemented and made available through the framework. Users can review archived results and compare them to the most recent snapshot of the operational environment. In our first iteration we have focused on visual data (FMV, WAMI, CCTV/PTZ-Cameras, open source video, etc.) and AIS data streams (satellite and terrestrial sources). As a proof-of-concept, in our experiments we show how FMV detections can be combined with vessel tracking signals from AIS sources to confirm identity, tip-and-cue aerial reconnaissance, and monitor vessel activity in an area.