A Physically Explainable Framework for Human-Related Anomaly Detection

Due to the complexity in understanding human behaviors under limited observations and insufficient training data, video anomaly detection is challenging. Most of existing approaches solely rely on visual clues and suffer from noisy observations which easily lead to unreasonable predictions. In this paper, we introduce physically explainable dynamics to enhance visual representations. Firstly, a Physical Intuition (PI) module is proposed to be combined with a Visual Representation (VR) module in estimating the forces applied on subjects. Secondly, a hierarchical structure is proposed to facilitate PI module in achieving physically plausible descriptions of human movements while maintaining the consistency with visual representations. Thirdly, a novel anomaly score is proposed considering the distributions of forces. Extensive experimental results on five benchmark datasets show that state-of-the-art performance can be achieved by the proposed framework with a strong robustness.