Papers › Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection

Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection

7 Dec 2021arXiv:2112.03649archive 2025-07-28

Shoubin Yu, Zhongyin Zhao, Haoshu Fang, Andong Deng, Haisheng Su, Dongliang Wang, Weihao Gan, Cewu Lu, Wei Wu

Anomaly detection in surveillance videos is challenging and important for ensuring public security. Different from pixel-based anomaly detection methods, pose-based methods utilize highly-structured skeleton data, which decreases the computational burden and also avoids the negative impact of background noise. However, unlike pixel-based methods, which could directly exploit explicit motion features such as optical flow, pose-based methods suffer from the lack of alternative dynamic representation. In this paper, a novel Motion Embedder (ME) is proposed to provide a pose motion representation from the probability perspective. Furthermore, a novel task-specific Spatial-Temporal Transformer (STT) is deployed for self-supervised pose sequence reconstruction. These two modules are then integrated into a unified framework for pose regularity learning, which is referred to as Motion Prior Regularity Learner (MoPRL). MoPRL achieves the state-of-the-art performance by an average improvement of 4.7% AUC on several challenging datasets. Extensive experiments validate the versatility of each proposed module.

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Code

Yui010206/MoPRL officialpytorch report

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Tasks

Anomaly Detection Anomaly Detection In Surveillance Videos Optical Flow Estimation Video Anomaly Detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Anomaly Detection	Corridor	MoPRL	AUC	71.63	#2 of 3	Archive leaderboard	report
Anomaly Detection	ShanghaiTech	MoPRL	AUC	83.35	#13 of 31	Archive leaderboard	report
Video Anomaly Detection	HR-ShanghaiTech	MoPRL	AUC	84.3	#2 of 14	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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