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Anomaly detection in surveillance videos is challenging and important forensuring public security. Different from pixel-based anomaly detection methods,pose-based methods utilize highly-structured skeleton data, which decreases thecomputational burden and also avoids the negative impact of background noise.However, unlike pixel-based methods, which could directly exploit explicitmotion features such as optical flow, pose-based methods suffer from the lackof alternative dynamic representation. In this paper, a novel Motion Embedder(ME) is proposed to provide a pose motion representation from the probabilityperspective. Furthermore, a novel task-specific Spatial-Temporal Transformer(STT) is deployed for self-supervised pose sequence reconstruction. These twomodules are then integrated into a unified framework for pose regularitylearning, which is referred to as Motion Prior Regularity Learner (MoPRL).MoPRL achieves the state-of-the-art performance by an average improvement of4.7% AUC on several challenging datasets. Extensive experiments validate theversatility of each proposed module.

Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection