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VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detection — arXiv:2608.05069

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.05069
License (as archived): CC0-1.0
Archived: 2026-10-31T11:15:20.700Z
SHA-256 of the archived text: 0a1667927c6c1bb958391dc1ac3d75ddab8264817941cc1dbd82326a9162e626
Stable URL of this page: https://metraipolis.com/documento/dc_5bb9804646eb

How to cite

VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detection — arXiv:2608.05069 — arXiv (en), https://arxiv.org/abs/2608.05069 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_5bb9804646eb (Metraipolis archive dc_5bb9804646eb, sha256 0a1667927c6c1bb958391dc1ac3d75ddab8264817941cc1dbd82326a9162e626)

BibTeX

@misc{dc5bb9804646eb,
  title = {VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detection — arXiv:2608.05069},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.05069},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_5bb9804646eb; sha256 0a1667927c6c1bb958391dc1ac3d75ddab8264817941cc1dbd82326a9162e626},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.05069. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Video Anomaly Detection (VAD) is inherently challenging due to the scarcity of anomalies and the large visual variability in surveillance footage, including changes in lighting, viewpoint, and human appearance. To mitigate visual noise and address privacy concerns, recent work has shifted to pose-based VAD, which focuses on motion dynamics rather than raw video data. However, existing pose-based approaches model human behavior in continuous latent spaces, limiting their ability to learn compact motion patterns necessary for robust behavior analysis. We address this by proposing Vector-Quantized Video Anomaly Detection (VQ-VAD), a novel human-centric anomaly detection framework that learns discrete motion representations. VQ-VAD adapts Vector-Quantized GAN (VQ-GAN), originally developed for image generation, to operate on keypoint sequences and construct a motion codebook of normal behavior. Trained exclusively on normal motion sequences, VQ-VAD detects anomalies by identifying high reconstruction errors when an observed motion sequence cannot be mapped to the learned codebook. We conduct extensive experiments across three complementary evaluation settings, including in-domain, cross-domain, and cross-dataset generalization, on four anomaly detection benchmarks. VQ-VAD achieves strong in-domain accuracy (81.83% on HR-SHT [15]), effective cross-domain transfer from CMU Panoptic [14] (76.69% on HR-SHT [15] without retraining), and competitive cross-dataset robustness. The code base for this work is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-06 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.05069 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T11:02:13.573Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-06?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).