ReeFRAME: Reeb Graph based Trajectory Analysis Framework to Capture Top-Down and Bottom-Up Patterns of Life

Chandrakanth Gudavalli, Bowen Zhang, Connor Levenson, Kin Gwn Lore, B. S. Manjunath
10/19/2024
cs.LG

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Abstract

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.

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Cite this paper

@article{gudavalli2024reeframe,
  title  = {ReeFRAME: Reeb Graph based Trajectory Analysis Framework to Capture Top-Down and Bottom-Up Patterns of Life},
  author = {Chandrakanth Gudavalli and Bowen Zhang and Connor Levenson and Kin Gwn Lore and B. S. Manjunath},
  year   = {2024},
  eprint = {2410.14913},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.14913v1}
}

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