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Anomaly Detection On Ucsd Ped2

Metrics

AUC

Results

Performance results of various models on this benchmark

Model Name
AUC
Paper TitleRepository
Two-stream97.1%Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection-
Background-Agnostic98.7%A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video-
STPT98.9%Spatio-temporal predictive tasks for abnormal event detection in videos-
DMAD99.7%Diversity-Measurable Anomaly Detection-
SD-MAE95.4%Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors-
MAMA98.2%Making Anomalies More Anomalous: Video Anomaly Detection Using a Novel Generator and Destroyer
VALD-GAN97.74VALD-GAN: video anomaly detection using latent discriminator augmented GAN-
ASTNet97.4%Attention-based residual autoencoder for video anomaly detection
FastAno96.3%FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation-
STemGAN97.5STemGAN: spatio-temporal generative adversarial network for video anomaly detection-
AnomalyRuler97.9%Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models-
MULDE-object-centric-micro99.7%MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection-
ConvVQ90.2%Diversity-Measurable Anomaly Detection-
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Anomaly Detection On Ucsd Ped2 | SOTA | HyperAI