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Video Anomaly Detection On Hr Shanghaitech

Metrics

AUC

Results

Performance results of various models on this benchmark

Model Name
AUC
Paper TitleRepository
BiPOCO74.9BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection
MoPRL84.3Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection
TrajREC77.9Holistic Representation Learning for Multitask Trajectory Anomaly Detection
COSKAD-euclidean77.1Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
MoCoDAD77.6Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
TSGAD81.77An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction
GEPC74.8Graph Embedded Pose Clustering for Anomaly Detection
Conv-AE69.8Learning Temporal Regularity in Video Sequences
MPED-RNN75.4Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos
Pred72.7Future Frame Prediction for Anomaly Detection -- A New Baseline
Multi-timescale Prediction77.0Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection-
COSKAD-radial75.2Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
PoseWatch-H87.23PoseWatch: A Transformer-based Architecture for Human-centric Video Anomaly Detection Using Spatio-temporal Pose Tokenization-
COSKAD-hyperbolic75.6Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
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Video Anomaly Detection On Hr Shanghaitech | SOTA | HyperAI