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Anomaly Detection In Surveillance Videos On

Metriken

Decidability

EER

ROC AUC

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Decidability	EER	ROC AUC	Paper Title	Repository
GMM-based	0.885	0.302	75.90	Weakly and Partially Supervised Learning Frameworks for Anomaly Detection
CR-UNet	-	-	85.24	Contrastive-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	-	-	84.48	A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection	-
MGFN	-	-	86.98	MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection
MIST	-	-	82.30	MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
Sultani et al.	0.613	0.353	75.41	Real-world Anomaly Detection in Surveillance Videos
MILR	-	-	76.67	3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos	-
PEL	-	-	86.76	Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection
RTFM	-	-	84.03	Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
S3R	-	-	85.99	Self-supervised Sparse Representation for Video Anomaly Detection
Multiple-Instance-Based-Video-Anomaly-Detection-Using-Deep-Temporal-Encoding-Decoding	-	-	80.10	Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding
BN-WVAD	-	-	87.24	BatchNorm-based Weakly Supervised Video Anomaly Detection
DMRMs	-	-	81.91	Anomalous Event Recognition in Videos Based on Joint Learningof Motion and Appearance with Multiple Ranking Measures	-
WSAL	-	-	85.38	Localizing Anomalies from Weakly-Labeled Videos

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