SeeKer models skeleton sequences as autoregressive Gaussian densities over individual keypoints and uses weighted negative log-likelihood as the anomaly score, achieving state-of-the-art AUROC on UBnormal and MSAD-HR.
Ssmtl++: Revisiting self-supervised multi-task learning for video anomaly detection
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Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detection
SeeKer models skeleton sequences as autoregressive Gaussian densities over individual keypoints and uses weighted negative log-likelihood as the anomaly score, achieving state-of-the-art AUROC on UBnormal and MSAD-HR.