Home Console Docs News Papers Tutorials Datasets Wiki SOTA LLM Models GPU Leaderboard Events

English

Video Anomaly Detection On Hr Shanghaitech

Metrics

AUC

Results

Performance results of various models on this benchmark

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

0 of 14 row(s) selected.

Video Anomaly Detection On Hr Shanghaitech | SOTA | HyperAI