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Video Anomaly Detection On Hr Shanghaitech

Métriques

AUC

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	AUC	Paper Title	Repository
BiPOCO	74.9	BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection
MoPRL	84.3	Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection
TrajREC	77.9	Holistic Representation Learning for Multitask Trajectory Anomaly Detection
COSKAD-euclidean	77.1	Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
MoCoDAD	77.6	Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
TSGAD	81.77	An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction
GEPC	74.8	Graph Embedded Pose Clustering for Anomaly Detection
Conv-AE	69.8	Learning Temporal Regularity in Video Sequences
MPED-RNN	75.4	Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos
Pred	72.7	Future Frame Prediction for Anomaly Detection -- A New Baseline
Multi-timescale Prediction	77.0	Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection	-
COSKAD-radial	75.2	Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
PoseWatch-H	87.23	PoseWatch: A Transformer-based Architecture for Human-centric Video Anomaly Detection Using Spatio-temporal Pose Tokenization	-
COSKAD-hyperbolic	75.6	Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection

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Video Anomaly Detection On Hr Shanghaitech | SOTA | HyperAI