  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.
