视频异常检测

Tiu MoAbout 3 min

✧ 视频异常检测 (Video Anomaly Detection)

➢ 论文汇总

[1] https://github.com/fjchange/awesome-video-anomaly-detectionopen in new window 该 repo 内有目前视频异常检测（VAD）方向的优秀论文汇总，包括基本分类、常用数据库下载、开源code、综述
[2] https://github.com/shot1107/anomaly_detection_papersopen in new window 该repo 内有异常检测每年顶会的论文，包括但不限于视频异常检测，可参考借鉴。

➢ 认识异常检测

1. 简单介绍（从异常行为检测--> 视频异常行为检测）

[1] 异常行为检测简介： https://mp.weixin.qq.com/s/UmT0DjFqRPsjv2m28ySvdwopen in new window [2] 基于深度学习的异常行为检测介绍：https://mp.weixin.qq.com/s/Aghbz4m1eWFCNGgEy8q6Cgopen in new window
[3] 基于深度学习的异常行为检测研究现状： https://mp.weixin.qq.com/s/MwpELRlC1cuDgqn4staAzAopen in new window
[4] 基于深度学习的视频异常行为事件检测简介: https://mp.weixin.qq.com/s/i3Xw2-ivARnF7rBSFtxugwopen in new window
[5] 基于视频的异常行为检测算法介绍: https://mp.weixin.qq.com/s/Dxsc3oCuO0wYkeFubMfSNwopen in new window

2.论文综述

[1] 邬开俊等. 视频异常检测技术研究进展[J]. 计算机科学与探索, 2022 （中文综述，但没有那么全面，可以有一个初步了解） [2] Bharathkumar Ramachandra et al. A survey of single-scene video anomaly detection (TPAMI 2020)

[1] A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework (ICCV 2017) -->proposed Shanghaitech dataset.
[2] Future Frame Prediction for Anomaly Detection – A New Baseline (CVPR 2018) [3] Future Frame Prediction for Anomaly Detection (TPAMI 2022)

■ Radu Ionescu SecurifAI/University of Bucharest

[1] Detecting abnormal events in video using Narrowed Normality Clusters (WACV 2019)
[2] Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video (CVPR 2019)
[3] Anomaly Detection in Video via Self-Supervised and Multi-Task Learning (CVPR 2021)
[4] A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video (TPAMI 2021)
[5] UBnormal New Benchmark for Supervised Open-Set Video Anomaly Detection (CVPR 2022) [6] Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection (CVPR 2022)

➢ 经典论文：（推荐加“👍”）

■ Unsupervised VAD

Conference Papers
[1] Learning Temporal Regularity in Video Sequences (CVPR 2016)
[2] A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework -->Proposed Shanghaitech dataset. [2] 👍Future Frame Prediction for Anomaly Detection -- A New Baseline (CVPR 2018)
[3] 👍Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (ICCV 2019) --> The first to employ memory module on video anomaly detection
[4] 👍Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection (CVPR 2019) --> The first to combine object detection and vad to achieve object-level anomaly dtection.
[5] AnoPCN: Video Anomaly Detection via Deep Predictive Coding Network (ACM MM 2019) --> The first hybrid model
[6] 👍Learning Memory-guided Normality for Anomaly Detection (CVPR 2020) --> Based on MemAE
[7] Cluster Attention Contrast for Video Anomaly Detection (ACM MM 2020) --> The first to apply Contrastive Learninig
[8] 👍Anomaly Detection in Video via Self-Supervised and Multi-Task Learning (CVPR 2021) --> object-level
[9] 👍A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction (ICCV 2021) --> Hybrid model [10] Anomaly Detection in Video Sequence with Appearance-Motion Correspondence (ICCV 2019) --> Two stream network
[11] Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder --> Two stream network
[12] Self-supervised Sparse Representation for Video Anomaly Detection (ECCV 2022) --> A first attempt to slove unsupervised and weakly supervised VAD [13] Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles (ECCV 2022)
Joural Papers
[1] Video Anomaly Detection with Sparse Coding Inspired Deep Neural Networks (TPAMI 2021) [2] A Background-Agnostic Framework With Adversarial Training for Abnormal Event Detection in Video (TPAMI 2022)
[3] Influence-Aware Attention Networks for Anomaly Detection in Surveillance Videos (TCSVT 2022)
[4] Bidirectional Spatio-Temporal Feature Learning With Multiscale Evaluation for Video Anomaly Detection (TCSVT 2022)
[5] Anomaly Detection With Bidirectional Consistency in Videos (TNNLS 2022)
[6] Variational Abnormal Behavior Detection With Motion Consistency (TIP 2022)
[7] DoTA: Unsupervised Detection of Traffic Anomaly in Driving Videos (TPAMI 2023) [8] A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos (TCSVT 2023)
[9] Learnable Locality-Sensitive Hashing for Video Anomaly Detection (TCSVT 2023)
[10] A Kalman Variational Autoencoder Model Assisted by Odometric Clustering for Video Frame Prediction and Anomaly Detection (TIP 2023) [11] Abnormal Event Detection and Localization via Adversarial Event Prediction (TNNLS 2023)

■ Weakly supervised VAD

[1] 👍 Real-world Anomaly Detection in Surveillance Videos (CVPR 2018)
[2] Weakly Supervised Video Anomaly Detection via Center-Guided Discrimative Learning (ICME 2020)

[3] Decouple and Resolve: Transformer-Based Models for Online Anomaly Detection From Weakly Labeled Videos (TIFS 2023)

➢ 经典项目

○ MNAD --> https://github.com/cvlab-yonsei/MNADopen in new window 可作为baseline.

➢ 发现的新的有意思的研究方向--> Explainable Anomaly Detection (EAD) 可解释性异常检测

1. DEFINITION

The aim of this TASK is to detect and automatically generate high-level explanations of anomalous events in video. Understanding the cause of an anomalous event is crucialas the required response is dependant on its nature andseverity. --> Anomaly Detection & Anoamly Explanation

[1] Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge (ICCV 2017)
[2] X-MAN: Explaining multiple sources of anomalies in video (CVPR workshop 2021)
[3] Discrete neural representations for explainable anomaly detection (WACV 2022)

视频异常检测