Papers › Normalizing Flows for Human Pose Anomaly Detection

Normalizing Flows for Human Pose Anomaly Detection

20 Nov 2022ICCV 2023 1arXiv:2211.10946archive 2025-07-28

Or Hirschorn, Shai Avidan

Video anomaly detection is an ill-posed problem because it relies on many parameters such as appearance, pose, camera angle, background, and more. We distill the problem to anomaly detection of human pose, thus decreasing the risk of nuisance parameters such as appearance affecting the result. Focusing on pose alone also has the side benefit of reducing bias against distinct minority groups. Our model works directly on human pose graph sequences and is exceptionally lightweight (~1K parameters), capable of running on any machine able to run the pose estimation with negligible additional resources. We leverage the highly compact pose representation in a normalizing flows framework, which we extend to tackle the unique characteristics of spatio-temporal pose data and show its advantages in this use case. The algorithm is quite general and can handle training data of only normal examples as well as a supervised setting that consists of labeled normal and abnormal examples. We report state-of-the-art results on two anomaly detection benchmarks - the unsupervised ShanghaiTech dataset and the recent supervised UBnormal dataset.

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Code

orhir/stg-nf officialmentioned in papermentioned on GitHubpytorchNOASSERTION report

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Tasks

Abnormal Event Detection In Video Anomaly Detection Anomaly Detection In Surveillance Videos Pose Estimation Self-Supervised Learning Video Anomaly Detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Anomaly Detection	ShanghaiTech	STG-NF	AUC	85.9%	#4 of 31	Archive leaderboard	report
Anomaly Detection	UBnormal	STG-NF - Supervised	AUC	79.2%	#1 of 14	Archive leaderboard	report
Anomaly Detection	UBnormal	STG-NF - Unsupervised	AUC	71.8%	#4 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

Convolution Normalizing Flows

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