用拓扑图分析人类轨迹，可实时检测群体与个人异常行为。
ReeFRAME: Reeb Graph based Trajectory Analysis Framework to Capture Top-Down and Bottom-Up Patterns of Life
- 基于重伯图构建群体与个体轨迹模型，捕捉上下文模式。
- 支持每秒1000点轨迹数据，处理50万代理2个月数据无延迟。
- 适合城市规划、公共安全等大规模轨迹分析场景。
本文提出ReeFRAME，一种基于重伯图的可扩展框架，用于分析高频GPS人类轨迹数据（1Hz）。该框架在群体和个体层面建模生活模式：使用多智能体重伯图（MARGs）刻画群体模式，时间重伯图（TERGs）描述个体轨迹。算法复杂度与时间点数呈线性关系，确保异常检测的实时性。我们在六个大规模异常检测数据集上验证了性能，模拟了持续两个月、最多50万代理的真实行为模式。
原文摘要 · Abstract (English)
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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