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.

介绍了一项名为人类行为异常检测（HAAD）的任务，旨在通过仅具有预先确定的正常训练动作样本类别，以无监督的方式识别异常运动。提出了一种基于归一化流（NF）的检测框架，可以有效地利用样本似然来指示异常。通过将动作样本从时域转换到频域，以减轻数据不稳定性的问题，以展示对记录期间潜在抖动数据的意识。在两个人类行为数据集上进行的大量实验结果表明，我们的方法优于将最先进的人类活动 AD 方法应用于我们的 HAAD 任务所形成的基线。