Group Anomaly Detection (GAD) reveals anomalous behavior among groups consisting of multiple member instances, which are, individually considered, not necessarily anomalous. This task is of major importance across multiple disciplines, in which also sequences like trajectories can be considered as a group. However, with increasing amount and heterogenity of group members, actual abnormal groups get harder to detect, especially in an unsupervised or semi-supervised setting. Recurrent Neural Networks are well established deep sequence models, but recent works have shown that their performance can decrease with increasing sequence lengths. Hence, we introduce with this paper GADFormer, a GAD specific BERT architecture, capable to perform attention-based Group Anomaly Detection on trajectories in an unsupervised and semi-supervised setting. We show formally and experimentally how trajectory outlier detection can be realized as an attention-based Group Anomaly Detection problem. Furthermore, we introduce a Block Attention-anomaly Score (BAS) to improve the interpretability of transformer encoder blocks for GAD. In addition to that, synthetic trajectory generation allows us to optimize the training for domain-specific GAD. In extensive experiments we investigate our approach versus GRU in their robustness for trajectory noise and novelties on synthetic and real world datasets.

翻译：群体异常检测（Group Anomaly Detection, GAD）旨在揭示由多个成员实例组成的群体中的异常行为，而单个实例本身未必异常。该任务在多个学科领域具有重要意义，其中诸如轨迹序列也可被视为一个群体。然而，随着群体成员数量增加及异质性增强，实际异常群体愈发难以检测，尤其是在无监督或半监督场景下。循环神经网络是成熟的深度序列模型，但近年研究表明其性能会随序列长度增加而下降。为此，本文提出GADFormer——一种针对GAD的BERT架构，能够在无监督和半监督环境下对轨迹执行基于注意力的群体异常检测。我们从理论与实验层面论证了轨迹离群点检测可被建模为基于注意力的群体异常检测问题。此外，我们引入块注意力异常分数（Block Attention-anomaly Score, BAS）以提升Transformer编码器模块在GAD中的可解释性。同时，合成轨迹生成技术使我们能够针对特定领域GAD优化训练过程。通过大量实验，我们在合成数据集与现实数据集上系统评估了所提方法与GRU在轨迹噪声鲁棒性和新异模式检测方面的表现。