PHEVA: A Privacy-preserving Human-centric Video Anomaly Detection Dataset
<|reference_start|>PHEVA: A Privacy-preserving Human-centric Video Anomaly Detection Dataset: 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.<|reference_end|>
@article{noghre2024pheva:,
    title={PHEVA: A Privacy-preserving Human-centric Video Anomaly Detection
  Dataset},
    author={Ghazal Alinezhad Noghre, Shanle Yao, Armin Danesh Pazho, Babak Rahimi
  Ardabili, Vinit Katariya, Hamed Tabkhi},
    journal={arXiv preprint arXiv:2408.14329},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14329},
    primaryClass={cs.CV cs.AI}
}