video-anomaly-detection

Papers for Video Anomaly Detection, released codes collection, Performance Comparision.

ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data.

Official code for 'Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning' [ICCV 2021]

🔥 🔥 🔥 [NeurIPS 2024] Hawk: Learning to Understand Open-World Video Anomalies

Official codes for CVPR2021 paper "MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection"

This is an official implementation for "Attention-based Residual Autoencoder for Video Anomaly Detection".

Useful Toolbox for Anomaly Detection

Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events. Oral paper in ACM Multimedia 2020.

An Attribute-based Method for Video Anomaly Detection (TMLR 2025)

Official code for AAAI2023 paper "Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection"

Frame level anomaly detection and localization in videos using auto-encoders

Anomaly Detection in Video via Self-Supervised and Multi-Task Learning

[CVPR 2025 Highlight] Official implementation of "Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity"

Pytorch Re-implement of ano_pre_cvpr2018, flownet2 / lite-flownet used.

A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video

This is an official implement for "HSTforU: Anomaly Detection in Aerial and Ground-based Videos with Hierarchical Spatio-Temporal Transformer for U-net"

[ICME 2025] Official implementation of "GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection"

[ICIP 2023] Exploring Diffusion Models For Unsupervised Video Anomaly Detection

This is the official implementation of the paper namely Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance Video.

Pytorch code for ECCVW 2022 paper "Consistency-based Self-supervised Learning for Temporal Anomaly Localization"