Human Kinematics-inspired Skeleton-based Video Anomaly Detection

TL;DR

This paper introduces HKVAD, a novel approach for video anomaly detection that explicitly uses human kinematic features like walking stride and skeleton displacement, validated on challenging datasets with promising results.

Contribution

The paper proposes a new kinematic-inspired method for anomaly detection that explicitly models human motion features, differing from implicit modeling approaches.

Findings

Effective detection of anomalies using kinematic features.

Achieves good results with minimal computational resources.

Validated on challenging public datasets.

Abstract

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.