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Anomaly Detection In Surveillance Videos On

评估指标

Decidability
EER
ROC AUC

评测结果

各个模型在此基准测试上的表现结果

模型名称
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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