We describe a clustering-based algorithm for tracking a dynamically varying number of targets observed by multiple sensors. The algorithm relies on discrete target detections (e.g., laser "hits") and a simple model of the targets to be tracked (e.g. a human is modeled in 2-D as a circle). The algorithm is evaluated in the context of a 4 versus 4 basketball game (8 targets) using 4 SICK LMS291 laser scanners as input. Our evaluations show that the sensor system correctly reports the number of targets roughly 99% of the time. We also demonstrate use of the tracker with two video datasets of multiple changing numbers of ants and fish, respectively

We view sensemaking in threat analysis as abducing a story whose plot explains the current data and makes predictions about the future. We have developed a preliminary computational system, called STAB, that abduces stories from data. STAB abduces multiple competing hypotheses by retrieving and instantiating story plots matching the current evidence. The story plots in STAB are represented as processes with goals and states, and organized in an abstraction hierarchy. STAB analyzes the VAST-2006 dataset. Given the VAST data incrementally, STAB generates multiple explanatory hypotheses, calculates their confidence values, and generates expectations about future data.