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Anomaly Detection In Surveillance Videos On

Métriques

Decidability
EER
ROC AUC

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Decidability
EER
ROC AUC
Paper TitleRepository
GMM-based0.8850.30275.90Weakly and Partially Supervised Learning Frameworks for Anomaly Detection
CR-UNet--85.24Contrastive-Regularized U-Net for Video Anomaly Detection-
STEAD-Fast--88.87--
MULDE-frame-centric-micro-one-class-classification--78.5%MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
STEAD-Base--91.34--
Multi-stream Network with Late Fuzzy Fusion--84.48A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection-
MGFN--86.98MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection
MIST--82.30MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
Sultani et al.0.6130.35375.41Real-world Anomaly Detection in Surveillance Videos
MILR--76.673D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos-
PEL--86.76Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection
RTFM--84.03Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
S3R--85.99Self-supervised Sparse Representation for Video Anomaly Detection
Multiple-Instance-Based-Video-Anomaly-Detection-Using-Deep-Temporal-Encoding-Decoding--80.10Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding
BN-WVAD--87.24BatchNorm-based Weakly Supervised Video Anomaly Detection
DMRMs--81.91Anomalous Event Recognition in Videos Based on Joint Learningof Motion and Appearance with Multiple Ranking Measures-
WSAL--85.38Localizing Anomalies from Weakly-Labeled Videos
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