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Papers/PHEVA: A Privacy-preserving Human-centric Video Anomaly De...

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

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

2024-08-26Continual LearningVideo Anomaly DetectionAnomaly DetectionPrivacy Preserving
PaperPDFCode(official)

Abstract

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.

Results

TaskDatasetMetricValueModel
Anomaly DetectionPHEVAAUC-ROC76.05MPED-RNN
Anomaly DetectionPHEVAAUC-ROC68TSGAD (Pose Branch)
Anomaly DetectionPHEVAAUC-ROC62.25GEPC
Anomaly DetectionPHEVAAUC-ROC57.57STG-NF

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