We introduce the task of human action anomaly detection (HAAD), which aims to identify anomalous motions in an unsupervised manner given only the pre-determined normal category of training action samples. Compared to prior human-related anomaly detection tasks which primarily focus on unusual events from videos, HAAD involves the learning of specific action labels to recognize semantically anomalous human behaviors. To address this task, we propose a normalizing flow (NF)-based detection framework where the sample likelihood is effectively leveraged to indicate anomalies. As action anomalies often occur in some specific body parts, in addition to the full-body action feature learning, we incorporate extra encoding streams into our framework for a finer modeling of body subsets. Our framework is thus multi-level to jointly discover global and local motion anomalies. Furthermore, to show awareness of the potentially jittery data during recording, we resort to discrete cosine transformation by converting the action samples from the temporal to the frequency domain to mitigate the issue of data instability. Extensive experimental results on two human action datasets demonstrate that our method outperforms the baselines formed by adapting state-of-the-art human activity AD approaches to our task of HAAD.

翻译：我们提出了人体动作异常检测（HAAD）任务，旨在仅利用预定的正常类别训练动作样本，以无监督方式识别异常运动。与以往主要关注视频中异常事件的人体相关异常检测任务相比，HAAD涉及学习特定动作标签以识别语义上异常的人体行为。为解决该任务，我们提出了一种基于归一化流（NF）的检测框架，有效利用样本似然来指示异常。由于动作异常常发生在特定身体部位，除了全身动作特征学习外，我们在框架中引入了额外的编码流以实现对身体子集的细粒度建模。因此，我们的框架具有多级结构，能够联合发现全局和局部运动异常。此外，为应对记录过程中可能存在的抖动数据，我们采用离散余弦变换将动作样本从时域转换到频域，以缓解数据不稳定性问题。在两个人体动作数据集上的广泛实验结果表明，我们的方法优于通过将最先进的人体活动AD方法适配到HAAD任务所构建的基线方法。