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Previous approaches to detecting human anomalies in videos have typicallyrelied on implicit modeling by directly applying the model to video or skeletondata, potentially resulting in inaccurate modeling of motion information. Inthis paper, we conduct an exploratory study and introduce a new idea calledHKVAD (Human Kinematic-inspired Video Anomaly Detection) for video anomalydetection, which involves the explicit use of human kinematic features todetect anomalies. To validate the effectiveness and potential of thisperspective, we propose a pilot method that leverages the kinematic features ofthe skeleton pose, with a specific focus on the walking stride, skeletondisplacement at feet level, and neck level. Following this, the method employsa normalizing flow model to estimate density and detect anomalies based on theestimated density. Based on the number of kinematic features used, we havedevised three straightforward variant methods and conducted experiments on twohighly challenging public datasets, ShanghaiTech and UBnormal. Our methodachieves good results with minimal computational resources, validating itseffectiveness and potential.

Human Kinematics-inspired Skeleton-based Video Anomaly Detection