There are 15 repositories under video-anomaly-detection topic.
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]
Official codes for CVPR2021 paper "MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection"
Useful Toolbox for Anomaly Detection
This is an official implementation for "Attention-based Residual Autoencoder for Video Anomaly Detection".
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events. Oral paper in ACM Multimedia 2020.
Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection
Frame level anomaly detection and localization in videos using auto-encoders
Official code for AAAI2023 paper "Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection"
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
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"
Official implementation of GlanceVAD
Pytorch code for ECCVW 2022 paper "Consistency-based Self-supervised Learning for Temporal Anomaly Localization"
This is the official implementation of the paper namely Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance Video.
AutoregressModel-AE_VAD_CVPR2019 (code reimplemetation)
[ICIP 2023] Exploring Diffusion Models For Unsupervised Video Anomaly Detection
Contains code and documentation for our VANE-Bench paper.
Deep Anomaly Discovery from Unlabeled Videos via Normality Advantage and Self-Paced Refinement (CVPR2022)
Masterthesis by Fabian Hofmann (2021, @TU-Berlin/DOS)
Official implementation of AAAI'24 paper "VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection"