A Behavior-Based Population Tracker Can Parse Aggregate Measurements to Differentiate Agents

Abstract

Closed-loop state estimators that track the movements and behaviors of large-scale populations have significant potential to benefit emergency teams during the critical early stages of disaster response. Such population trackers could enable insight about the population even where few direct measurements are available. In concept, a population tracker might be realized using a Bayesian estimation framework to fuse agent-based models of human movement and behavior with sparse sensing, such as a small set of cameras providing population counts at specific locations. We describe a simple proof-of-concept for such an estimator by applying a particle-filter to synthetic sensor data generated from a small simulated environment. An interesting result is that behavioral models embedded in the particle filter make it possible to distinguish among simulated agents, even when the only available sensor data are aggregate population counts at specific locations.