Papers › PHEVA: A Privacy-preserving Human-centric Video Anomaly Detection Dataset

PHEVA: A Privacy-preserving Human-centric Video Anomaly Detection Dataset

26 Aug 2024arXiv:2408.14329archive 2025-07-28

Ghazal Alinezhad Noghre, Shanle Yao, Armin Danesh Pazho, Babak Rahimi Ardabili, Vinit Katariya, Hamed Tabkhi

PHEVA, a Privacy-preserving Human-centric Ethical Video Anomaly detection dataset. By removing pixel information and providing only de-identified human annotations, PHEVA safeguards personally identifiable information. The dataset includes seven indoor/outdoor scenes, featuring one novel, context-specific camera, and offers over 5x the pose-annotated frames compared to the largest previous dataset. This study benchmarks state-of-the-art methods on PHEVA using a comprehensive set of metrics, including the 10% Error Rate (10ER), a metric used for anomaly detection for the first time providing insights relevant to real-world deployment. As the first of its kind, PHEVA bridges the gap between conventional training and real-world deployment by introducing continual learning benchmarks, with models outperforming traditional methods in 82.14% of cases. The dataset is publicly available at https://github.com/TeCSAR-UNCC/PHEVA.git.

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Tasks

Anomaly Detection Continual Learning Privacy Preserving Video Anomaly Detection

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Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Anomaly Detection	PHEVA	MPED-RNN	AUC-ROC	76.05	#1 of 4	Archive leaderboard	report
Anomaly Detection	PHEVA	TSGAD (Pose Branch)	AUC-ROC	68	#2 of 4	Archive leaderboard	report
Anomaly Detection	PHEVA	GEPC	AUC-ROC	62.25	#3 of 4	Archive leaderboard	report
Anomaly Detection	PHEVA	STG-NF	AUC-ROC	57.57	#4 of 4	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.

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