  Previous approaches to detecting human anomalies in videos have typically
relied on implicit modeling by directly applying the model to video or skeleton
data, potentially resulting in inaccurate modeling of motion information. In
this paper, we conduct an exploratory study and introduce a new idea called
HKVAD (Human Kinematic-inspired Video Anomaly Detection) for video anomaly
detection, which involves the explicit use of human kinematic features to
detect anomalies. To validate the effectiveness and potential of this
perspective, we propose a pilot method that leverages the kinematic features of
the skeleton pose, with a specific focus on the walking stride, skeleton
displacement at feet level, and neck level. Following this, the method employs
a normalizing flow model to estimate density and detect anomalies based on the
estimated density. Based on the number of kinematic features used, we have
devised three straightforward variant methods and conducted experiments on two
highly challenging public datasets, ShanghaiTech and UBnormal. Our method
achieves good results with minimal computational resources, validating its
effectiveness and potential.
