Intention-aware Hierarchical Diffusion Model for Long-term Trajectory Anomaly Detection

• URL: http://arxiv.org/abs/2509.17068v1
• Date: Sun, 21 Sep 2025 12:57:22 GMT
• Title: Intention-aware Hierarchical Diffusion Model for Long-term Trajectory Anomaly Detection
• Authors: Chen Wang, Sarah Erfani, Tansu Alpcan, Christopher Leckie,
• Abstract summary: We propose an unsupervised trajectory anomaly detection method named Intention-aware Hierarchical Diffusion model (IHiDgoal)<n>IHiDgoal detects anomalies through both high-level intent evaluation and low-level sub-trajectory analysis.<n>Our experiments show that the proposed method IHiDgoal achieves up to 30.2% improvement in anomaly detection performance in terms of F1 score over state-of-the-art baselines.
• Score: 9.366406753448453
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-term trajectory anomaly detection is a challenging problem due to the diversity and complex spatiotemporal dependencies in trajectory data. Existing trajectory anomaly detection methods fail to simultaneously consider both the high-level intentions of agents as well as the low-level details of the agent's navigation when analysing an agent's trajectories. This limits their ability to capture the full diversity of normal trajectories. In this paper, we propose an unsupervised trajectory anomaly detection method named Intention-aware Hierarchical Diffusion model (IHiD), which detects anomalies through both high-level intent evaluation and low-level sub-trajectory analysis. Our approach leverages Inverse Q Learning as the high-level model to assess whether a selected subgoal aligns with an agent's intention based on predicted Q-values. Meanwhile, a diffusion model serves as the low-level model to generate sub-trajectories conditioned on subgoal information, with anomaly detection based on reconstruction error. By integrating both models, IHiD effectively utilises subgoal transition knowledge and is designed to capture the diverse distribution of normal trajectories. Our experiments show that the proposed method IHiD achieves up to 30.2% improvement in anomaly detection performance in terms of F1 score over state-of-the-art baselines.

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