面向人类语义轨迹的可迁移无监督异常检测框架
Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories
AI总结：
针对传统异常检测依赖领域知识且缺乏多模态综合考量的问题，提出可迁移无监督异常检测框架TOD4Traj，通过模态特征统一与对比学习实现跨数据集的人类轨迹异常检测，性能优于现有模型。
摘要：
语义轨迹通过添加出行目的或位置活动等文本信息来丰富时空数据，是识别对医疗保健、社会保障和城市规划至关重要的异常行为的关键。传统的异常检测依赖于启发式规则，这需要领域知识并限制了其识别未见异常的能力。此外，缺乏一种能够联合考虑空间、时间和文本维度多模态数据的综合方法。为了满足对领域无关模型的需求，我们提出了面向人类语义轨迹的可迁移异常检测（TOD4Traj）框架。TOD4Traj首先引入了模态特征统一模块来对齐不同的数据特征表示，从而实现多模态信息的集成，并增强跨不同数据集的可迁移性。进一步提出了对比学习模块，用于在时间上和跨人群中识别规律性移动模式，从而允许基于个体一致性和群体多数模式联合检测异常。实验结果表明，TOD4Traj的性能优于现有模型，证明了其在检测跨各种数据集的人类轨迹异常方面的有效性和适应性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
