ReeFRAME: Reeb Graph based Trajectory Analysis Framework to Capture Top-Down and Bottom-Up Patterns of Life
This work addresses the challenge of scalable anomaly detection in human trajectory data for applications like surveillance or urban planning, but it appears incremental as it builds on existing Reeb graph methods.
The paper tackles the problem of analyzing large-scale GPS trajectory data by introducing ReeFRAME, a scalable Reeb graph-based framework that models Patterns-of-life at population and individual levels, achieving linear algorithmic complexity and validating it on datasets with up to 500,000 agents over two months.
In this paper, we present ReeFRAME, a scalable Reeb graph-based framework designed to analyze vast volumes of GPS-enabled human trajectory data generated at 1Hz frequency. ReeFRAME models Patterns-of-life (PoL) at both the population and individual levels, utilizing Multi-Agent Reeb Graphs (MARGs) for population-level patterns and Temporal Reeb Graphs (TERGs) for individual trajectories. The framework's linear algorithmic complexity relative to the number of time points ensures scalability for anomaly detection. We validate ReeFRAME on six large-scale anomaly detection datasets, simulating real-time patterns with up to 500,000 agents over two months.