跨数据集检测人类轨迹异常，融合时空文本多模态信息
Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories
- 通过模态对齐统一多源轨迹特征，提升跨域适应性
- 利用对比学习捕捉群体主流与个体一致性模式，精准识别异常
- 无需领域知识，适用于医疗、城市规划等多场景
语义轨迹通过添加出行目的或位置活动等文本信息，丰富了时空数据，对识别医疗、社会安全与城市规划中的异常行为至关重要。传统方法依赖启发式规则，需领域知识且难以发现未见异常。现有方法缺乏对空间、时间与文本多维度的联合建模。为此，本文提出可迁移的人类语义轨迹异常检测框架TOD4Traj。该框架首先设计模态特征统一模块，对齐异构数据特征表示，实现多模态信息融合并增强跨数据集迁移能力；进一步引入对比学习模块，联合捕捉时空层面的常规移动模式及群体共性，基于个体一致性与群体多数模式实现异常检测。实验表明，TOD4Traj在多个数据集上均优于现有模型，具备优异性能与广泛适用性。
原文摘要 · Abstract (English)
Semantic trajectories, which enrich spatial-temporal data with textual information such as trip purposes or location activities, are key for identifying outlier behaviors critical to healthcare, social security, and urban planning. Traditional outlier detection relies on heuristic rules, which requires domain knowledge and limits its ability to identify unseen outliers. Besides, there lacks a comprehensive approach that can jointly consider multi-modal data across spatial, temporal, and textual dimensions. Addressing the need for a domain-agnostic model, we propose the Transferable Outlier Detection for Human Semantic Trajectories (TOD4Traj) framework.TOD4Traj first introduces a modality feature unification module to align diverse data feature representations, enabling the integration of multi-modal information and enhancing transferability across different datasets. A contrastive learning module is further pro-posed for identifying regular mobility patterns both temporally and across populations, allowing for a joint detection of outliers based on individual consistency and group majority patterns. Our experimental results have shown TOD4Traj's superior performance over existing models, demonstrating its effectiveness and adaptability in detecting human trajectory outliers across various datasets.
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