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

• URL: http://arxiv.org/abs/2408.14329v1
• Date: Mon, 26 Aug 2024 14:55:23 GMT
• Title: PHEVA: A Privacy-preserving Human-centric Video Anomaly Detection Dataset
• Authors: Ghazal Alinezhad Noghre, Shanle Yao, Armin Danesh Pazho, Babak Rahimi Ardabili, Vinit Katariya, Hamed Tabkhi,
• Abstract summary: PHEVA safeguards personally identifiable information by removing pixel information and providing only de-identified human annotations. This study benchmarks state-of-the-art methods on PHEVA using a comprehensive set of metrics, including the 10% Error Rate (10ER) As the first of its kind, PHEVA bridges the gap between conventional training and real-world deployment by introducing continual learning benchmarks.
• Score: 2.473948454680334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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