Anomaly Detection On Shanghaitech

Métriques

AUC

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	AUC	Paper Title	Repository
AnomalyRuler	85.2%	Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models	-
VideoPatchCore	85.1%	VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection	-
TSGAD	80.6%	An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction	-
PGM	61.28%	Bounding Boxes and Probabilistic Graphical Models: Video Anomaly Detection Simplified	-
EVAL	76.63%	EVAL: Explainable Video Anomaly Localization	-
SSMTL+UBnormal	83.7%	UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection	-
MPED-RNN	73.40%	Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos	-
Jigsaw-VAD	84.3%	Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles	-
Multi-timescale Prediction	76.03%	Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection	-
Any-Shot Sequential	71.6%	Any-Shot Sequential Anomaly Detection in Surveillance Videos	-
Background- Agnostic Framework+SSPCAB	83.6%	Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection	-
SSMTL++v2	83.8%	SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection	-
STAN	76.2%	STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection	-
ASTNet	73.6	Attention-based residual autoencoder for video anomaly detection
two-stream	83.7%	Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection	-
Sparse Coding Stacked RNN	68.0%	A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework
STPT	77.1%	Spatio-temporal predictive tasks for abnormal event detection in videos	-
DMAD	78.8%	Diversity-Measurable Anomaly Detection	-
DAC(STG-NF + Jigsaw)	87.72%	Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New Approach	-
MULDE-object-centric-micro	86.7%	MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection	-

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