UHPose-VAD: Unsupervised Video Anomaly Detection via Pose-Graph Learning and Normalizing Flow.

Jiang D, Lai H, Gao G, Ma D, Wang L.

Open source

DOI: 10.3390/jimaging12060227
Published: 2026-05-27
Container: J Imaging
Publisher: Not recorded
Open access: no

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Cite this work

BibTeX

@article{allodium:10.3390/jimaging12060227,
  title = {UHPose-VAD: Unsupervised Video Anomaly Detection via Pose-Graph Learning and Normalizing Flow.},
  author = {Jiang D and  Lai H and  Gao G and  Ma D and  Wang L.},
  year = {2026},
  journal = {J Imaging},
  doi = {10.3390/jimaging12060227},
  url = {https://doi.org/10.3390/jimaging12060227}
}

RIS

TY  - JOUR
TI  - UHPose-VAD: Unsupervised Video Anomaly Detection via Pose-Graph Learning and Normalizing Flow.
AU  - Jiang D
AU  -  Lai H
AU  -  Gao G
AU  -  Ma D
AU  -  Wang L.
PY  - 2026
JO  - J Imaging
DO  - 10.3390/jimaging12060227
UR  - https://doi.org/10.3390/jimaging12060227
ER  -

APA

D, J., H, L., G, G., D, M., & L., W. (2026). UHPose-VAD: Unsupervised Video Anomaly Detection via Pose-Graph Learning and Normalizing Flow.. J Imaging. https://doi.org/10.3390/jimaging12060227

Source records

europe-pmc · retrieved 2026-09-27T02:32:20.140Z
doaj · retrieved 2026-09-27T02:32:20.139Z