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.

翻译：先前视频中检测人体异常的方法通常依赖于隐式建模，即直接将模型应用于视频或骨架数据，可能导致运动信息建模不准确。本文开展探索性研究，提出一种称为HKVAD（基于人体运动学的视频异常检测）的新思路，通过显式利用人体运动学特征进行异常检测。为验证该视角的有效性和潜力，我们提出一种先导方法，该方法利用骨架姿态的运动学特征，重点关注步行步幅、脚部层面骨架位移及颈部层面位移。随后，该方法采用归一化流模型进行密度估计，并基于估计密度检测异常。根据所用运动学特征数量，我们设计了三种简单的变体方法，并在两个高难度公开数据集ShanghaiTech和UBnormal上开展实验。本方法以极低计算资源取得良好效果，验证了其有效性与潜力。