Group Anomaly Detection (GAD) identifies unusual pattern in groups where individual members might not be anomalous. This task is of major importance across multiple disciplines, in which also sequences like trajectories can be considered as a group. As groups become more diverse in heterogeneity and size, detecting group anomalies becomes challenging, especially without supervision. Though Recurrent Neural Networks are well established deep sequence models, their performance can decrease with increasing sequence lengths. Hence, this paper introduces GADformer, a BERT-based model for attention-driven GAD on trajectories in unsupervised and semi-supervised settings. We demonstrate how group anomalies can be detected by attention-based GAD. We also introduce the Block-Attention-anomaly-Score (BAS) to enhance model transparency by scoring attention patterns. In addition to that, synthetic trajectory generation allows various ablation studies. In extensive experiments we investigate our approach versus related works in their robustness for trajectory noise and novelties on synthetic data and three real world datasets.

翻译：群体异常检测（GAD）旨在识别群体中个体成员可能并非异常但整体呈现异常模式的现象。该任务在多个学科领域具有重要价值，其中序列数据（如轨迹）也可被视为群体进行分析。随着群体在异质性和规模上的多样化，无监督场景下的群体异常检测变得尤为困难。尽管循环神经网络是成熟的深度序列模型，但其性能会随序列长度增加而下降。为此，本文提出GADformer——一种基于BERT的模型，用于在无监督和半监督设置下对轨迹进行注意力驱动的群体异常检测。我们展示了如何通过注意力机制检测群体异常，并引入块注意力异常分数（BAS），通过为注意力模式评分来增强模型透明度。此外，合成轨迹生成技术支持多种消融研究。通过大量实验，我们在合成数据和三个真实世界数据集上考察了所提方法与相关工作在轨迹噪声鲁棒性和新颖性方面的表现。