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Awesome Video Anomaly Detection

GitHub License Awesome

Video anomaly detection (VAD) aims to identify anomalous frames within given videos, which servers a vital function in critical areas, e.g., public security, media content monitoring and industrial manufacture. This repository collects latest research papers, code, datasets, utilities and related resources for VAD.

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Contents

Recent Updates

Last Update: October 21, 2024

  • ACM MM 24'
  • ECCV 24'
  • CVPR 24'

New Setting Papers

Toward Video Anomaly Retrieval From Video Anomaly Detection: New Benchmarks and Model
Peng Wu, Jing Liu, Xiangteng He, Yuxin Peng, Peng Wang, Yanning Zhang
I3D with-Audio
TIP 24' [paper][dataset]

Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models
Yuchen Yang, Kwonjoon Lee, Behzad Dariush, Yinzhi Cao, Shao-Yuan Lo
LLM
ECCV 24' [paper][code]

Open-Vocabulary Video Anomaly Detection
Peng Wu, Xuerong Zhou, Guansong Pang, Yujia Sun, Jing Liu, Peng Wang, Yanning Zhang
CLIP-V CLIP-T LLM
CVPR 24' [paper][supp]

Harnessing Large Language Models for Training-free Video Anomaly Detection
Luca Zanella, Willi Menapace, Massimiliano Mancini, Yiming Wang, Elisa Ricci
LLM
CVPR 24' [paper][code][supp]

Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly
Hang Du, Sicheng Zhang, Binzhu Xie, Guoshun Nan, Jiayang Zhang, Junrui Xu, Hangyu Liu, Sicong Leng, Jiangming Liu, Hehe Fan, Dajiu Huang, Jing Feng, Linli Chen, Can Zhang, Xuhuan Li, Hao Zhang, Jianhang Chen, Qimei Cui, Xiaofeng Tao
LLM
CVPR 24' [paper][code & dataset][supp]

TDSD: Text-Driven Scene-Decoupled Weakly Supervised Video Anomaly Detection
Shengyang Sun, Jiashen Hua, Junyi Feng, Dongxu Wei, Baisheng Lai, Xiaojin Gong
I3D CLIP-V CLIP-T
ACM MM 24' [paper][code][OpenReview]

Weakly-supervised VAD Papers

Cross-Domain Learning for Video Anomaly Detection with Limited Supervision
Yashika Jain, Ali Dabouei, Min Xu
I3D CLIP-V
ECCV 24' [paper]

Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video Anomaly Detection
Ayush Ghadiya, Purbayan Kar, Vishal Chudasama, Pankaj Wasnik
I3D with-Audio
CVPR 24' Workshop [paper]

Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts
Peng Wu, Xuerong Zhou, Guansong Pang, Zhiwei Yang, Qingsen Yan, Peng Wang, Yanning Zhang
CLIP-V CLIP-T
ACM MM 24' [paper][OpenReview]

Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection
Chen Zhang, Guorong Li, Yuankai Qi, Shuhui Wang, Laiyun Qing, Qingming Huang, Ming-Hsuan Yang
I3D
CVPR 23' [paper][code][supp]

Look Around for Anomalies: Weakly-supervised Anomaly Detection via Context-Motion Relational Learning
MyeongAh Cho, Minjung Kim, Sangwon Hwang, Chaewon Park, Kyungjae Lee, Sangyoun Lee
I3D
CVPR 23' [paper][supp]

Prompt Involved Papers

Vadclip: Adapting vision-language models for weakly supervised video anomaly detection
Peng Wu, Xuerong Zhou, Guansong Pang, Lingru Zhou, Qingsen Yan, Peng Wang, Yanning Zhang
CLIP-V CLIP-T
AAAI 24' [paper][code]

Prompt-Enhanced Multiple Instance Learning for Weakly Supervised Video Anomaly Detection
Junxi Chen, Liang Li , Li Su , Zheng-Jun Zha, Qingming Huang
I3D CLIP-T with-Audio
CVPR 24' [paper][code][supp]

Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection
Zhiwei Yang, Jing Liu , Peng Wu
CLIP-V CLIP-T
CVPR 24' [paper][supp]

Semi-supervised VAD Papers

Learning Anomalies with Normality Prior for Unsupervised Video Anomaly Detection
Haoyue Shi, Le Wang, Sanping Zhou, Gang Hua, Wei Tang
ResNext
ECCV 24' [paper]

Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection
Yongwei Nie, Hao Huang, Chengjiang Long, Qing Zhang, Pradipta Maji, Hongmin Cai
I3D
ECCV 24' [paper][code]

Scene-Dependent Prediction in Latent Space for Video Anomaly Detection and Anticipation
Congqi Cao, Hanwen Zhang, Yue Lu, Peng Wang, Yanning Zhang
T-PAMI 24'[paper][project][code][dataset]

DoTA: Unsupervised Detection of Traffic Anomaly in Driving Videos
Yu Yao, Xizi Wang, Mingze Xu, Zelin Pu, Yuchen Wang, Ella Atkins, Senior Member, David J. Crandall
T-PAMI 23' [paper][code][dataset]

Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors
Nicolae-Cătălin Ristea, Florinel-Alin Croitoru, Radu Tudor Ionescu, Marius Popescu, Fahad Shahbaz Khan, Mubarak Shah
CVPR 24' [paper][code][supp]

Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal Representation Learning
I3D
Menghao Zhang, Jingyu Wang, Qi Qi, Haifeng Sun, Zirui Zhuang, Pengfei Ren, Ruilong Ma, Jianxin Liao
CVPR 24' [paper][supp]

MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
Jakub Micorek Horst Possegger Dominik Narnhofer Horst Bischof Mateusz Kozínski
CVPR 24' [paper][code][supp]

Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline
Anas Al-lahham, Muhammad Zaigham Zaheer, Nubrek Tastan, Karthik Nandakumar
CVPR 24' [paper][code][supp]

A Multilevel Guidance-Exploration Network and Behavior-Scene Matching Method for Human Behavior Anomaly Detection
Guoqing Yang, Zhiming Luo, Jianzhe Gao, Yingxin Lai, Kun Yang, Yifan He, Shaozi Li
SwinTrans
ACM MM 24' [paper][code][OpenReview]

Video Anomaly Detection via Progressive Learning of Multiple Proxy Tasks
Menghao Zhang, Jingyu Wang, Qi Qi, Pengfei Ren, Haifeng Sun, Zirui Zhuang, Huazheng Wang, Lei Zhang, Jianxin Liao
ACM MM 24' [paper][OpenReview]

Fully-supervised VAD Papers

Exploring Background-bias for Anomaly Detection in Surveillance Videos
Kun Liu, Huadong Ma
ACM MM 19' [paper][annotation]

ANOMALY LOCALITY IN VIDEO SURVEILLANCE
Federico Landi, Cees G.M.Snoek, Rita Cucchiara
arXiv 19' [paper][project][annotation]

Surveys

Weakly Supervised Anomaly Detection: A Survey
Minqi Jiang, Chaochuan Hou, Ao Zheng, Xiyang Hu, Songqiao Han, Hailiang Huang, Xiangnan He , Philip S. Yu, Yue Zhao
arXiv 23' [paper][repo]

Video Anomaly Detection in 10 Years: A Survey and Outlook
SAJID Moshira Abdalla, Sajid Javed, Muaz Al Radi, Anwaar Ulhaq, Naoufel Werghi
arXiv 24' [paper]

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I. Webb, Irwin King, Shirui Pan
T-PAMI 24' [paper][repo]

Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis
Mohammad Sabbaqi and Elvin Isufi
T-PAMI 23' [paper]

Datasets

Dataset Download Links Features Frame-level Annotation Publication
ShanghaiTech Campus BaiduYun I3D RGB - CVPR 18'
UCF-Crime Dropbox I3D RGB Link CVPR 18'
XD-Violence OneDrive I3D RGB & VGGish - ECCV 20'

Utilities

[Video & Audio Feature Extraction] video_features: it allows you to extract features from video clips, supporting a variety of modalities and extractors, i.e., S3D, R(2+1)d RGB, I3D-Net RGB + Flow, VGGish, CLIP.

Related Repositories

awesome-video-anomaly-detection: an awesome collection of papers and codes for video anomaly detection, updated to CVPR 22'.

WSAD: a comprehensive collection and categorization of weakly supervised anomaly detection papers.

awesome anomaly detection: a curated list of awesome anomaly detection resources, including time-series anomaly detection, video-level anomaly detection, image-level anomaly detection.

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