/awesome-video-anomaly-detection

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

awesome-video-anomaly-detection Awesome

Papers for Video Anomaly Detection, released codes collections.

Any addition or bug please open an issue, pull requests or e-mail me by fjchange@hotmail.com

Datasets

  1. UMN Download link
  2. UCSD Download link
  3. Subway Entrance/Exit Download link
  4. CUHK Avenue Download link
  5. ShanghaiTech Download link
  6. UCF-Crime (Weakly Supervised)
  7. Traffic-Train
  8. Belleview
  9. Street Scene (WACV 2020) Street Scenes, Download link
  10. IITB-Corridor (WACV 2020) Rodrigurs.etl
  11. XD-Violence (ECCV 2020) XD-ViolenceDownload link

The Datasets belowed are about Traffic Accidents Anticipating in Dashcam videos or Surveillance videos

  1. CADP (CarCrash Accidents Detection and Prediction)
  2. DAD paper, Download link
  3. A3D paper, Download link
  4. DADA Download link
  5. DoTA Download_link
  6. Iowa DOT Download_link

Unsupervised

2016

  1. [Conv-AE] Learning Temporal Regularity in Video Sequences, CVPR 16. Code

2017

  1. [Hinami.etl] Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge, ICCV 2017. (Explainable VAD)
  2. [Stacked-RNN] A revisit of sparse coding based anomaly detection in stacked rnn framework, ICCV 2017. code
  3. [ConvLSTM-AE] Remembering history with convolutional LSTM for anomaly detection, ICME 2017.Code
  4. [Conv3D-AE] Spatio-Temporal AutoEncoder for Video Anomaly Detection,ACM MM 17.
  5. [Unmasking] Unmasking the abnormal events in video, ICCV 17.
  6. [DeepAppearance] Deep appearance features for abnormal behavior detection in video

2018

  1. [FramePred] Future Frame Prediction for Anomaly Detection -- A New Baseline, CVPR 2018. code
  2. [ALOOC] Adversarially Learned One-Class Classifier for Novelty Detection, CVPR 2018. code
  3. Detecting Abnormality Without Knowing Normality: A Two-stage Approach for Unsupervised Video Abnormal Event Detection, ACM MM 18.

2019

  1. [Mem-AE] Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection, ICCV 2019.code
  2. [Skeleton-based] Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos, CVPR 2019.code
  3. [Object-Centric] Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection, CVPR 2019.
  4. [Appearance-Motion Correspondence] Anomaly Detection in Video Sequence with Appearance-Motion Correspondence, ICCV 2019.code
  5. [AnoPCN]AnoPCN: Video Anomaly Detection via Deep Predictive Coding Network, ACM MM 2019.

2020

  1. [Street-Scene] Street Scene: A new dataset and evaluation protocol for video anomaly detection, WACV 2020.
  2. [Rodrigurs.etl]) Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection, WACV 2020.
  3. [GEPC] Graph Embedded Pose Clustering for Anomaly Detection, CVPR 2020.code
  4. [Self-trained] Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection, CVPR 2020.
  5. [MNAD] Learning Memory-guided Normality for Anomaly Detection, CVPR 2020. code
  6. [Continual-AD]] Continual Learning for Anomaly Detection in Surveillance Videos,CVPR 2020 Worksop.
  7. [OGNet] Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm, CVPR 2020. code
  8. [Any-Shot] Any-Shot Sequential Anomaly Detection in Surveillance Videos,CVPR 2020 workshop.
  9. [Few-Shot]Few-Shot Scene-Adaptive Anomaly DetectionECCV 2020 Spotlight code
  10. [CDAE]Clustering-driven Deep Autoencoder for Video Anomaly DetectionECCV 2020
  11. [VEC]Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video EventsACM MM 2020 Oral code

Weakly-Supervised

2018

  1. [Sultani.etl] Real-world Anomaly Detection in Surveillance Videos, CVPR 2018 code

2019

  1. [GCN-Anomaly] Graph Convolutional Label Noise Cleaner:Train a Plug-and-play Action Classifier for Anomaly Detection, CVPR 2019, code
  2. [MLEP] Margin Learning Embedded Prediction for Video Anomaly Detection with A Few Anomalies, IJCAI 2019.
  3. [IBL] Temporal Convolutional Network with Complementary Inner Bag Loss For Weakly Supervised Anomaly Detection. ICIP 19.
  4. [Motion-Aware] Motion-Aware Feature for Improved Video Anomaly Detection. BMVC 19.

2020

  1. [Siamese] Learning a distance function with a Siamese network to localize anomalies in videos, WACV 2020.
  2. [AR-Net] Weakly Supervised Video Anomaly Detection via Center-Guided Discrimative Learning, ICME 2020.code
  3. ['XD-Violence'] Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision ECCV 2020

Supervised

2019

  1. [Background-Bias]Exploring Background-bias for Anomaly Detection in Surveillance Videos, ACM MM 19.
  2. [Ano-Locality]Anomaly locality in video suveillance.

Reviews / Surveys

  1. An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos, J. Image, 2018.page
  2. DEEP LEARNING FOR ANOMALY DETECTION: A SURVEY, paper
  3. Video Anomaly Detection for Smart Surveillance paper

Books

  1. Outlier Analysis. Charu C. Aggarwal

Generally, anomaly detection in recent researchs are based on the datasets get from pedestrian (likes UCSD, Avenue, ShanghaiTech, etc.)， or UCF-Crime (real-wrold anomaly). However some focus on specefic scene as follows.

Specific Scene

Traffic

CVPR 2018 workshop, CVPR 2019 workshop, AICity Challenge series.

First-Person Traffic

  1. Unsupervised Traffic Accident Detection in First-Person Videos, IROS 2019.

Driving

When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos. github

Old-man Fall Down

Fighting/Violence

  1. Localization Guided Fight Action Detection in Survellance Videos. ICME 2019.

Social/ Group Anomaly

  1. Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks, Neurips 2019.

Related Topics:

  1. Video Representation (Unsupervised Video Representation, reconstruction, prediction etc.)
  2. Object Detection
  3. Pedestrian Detection
  4. Skeleton Detection
  5. Graph Neural Networks
  6. GAN
  7. Action Recongnition / Temporal Action Localization
  8. Metric Learning
  9. Label Noise Learning
  10. Cross-Modal/ Multi-Modal
  11. Dictionary Learning
  12. One-Class Classification / Novelty Detection / Out-of-Disturibution Detection
  13. Action Recognition.
    • Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events. ACM MM 2020 workshop.

Performance Evaluation Methods

  1. AUC
  2. PR-AUC
  3. Score Gap
  4. False Alarm Rate on Normal with 0.5 as threshold (Weakly supervised, proposed in CVPR 18)

Performance Comparision on UCF-Crime

Model Reported on Convference/Journal Supervised Feature End2End 32 Segments AUC (%) FAR@0.5 on Normal (%)
Sultani.etl CVPR 18 Weakly C3D RGB X √ 75.41 1.9
IBL ICIP 19 Weakly C3D RGB X √ 78.66 -
Motion-Aware BMVC 19 Weakly PWC Flow X √ 79.0 -
GCN-Anomaly CVPR 19 Weakly TSN RGB √ X 82.12 0.1
Background-Bias ACM MM 19 Fully NLN RGB √ X 82.0 -

Perfromace Comparision on ShanghaiTech

Model Reported on Conference/Journal Supervision Feature End2End AUC(%) FAR@0.5 (%)
Conv-AE CVPR 16 Un - √ 60.85 -
stacked-RNN ICCV 17 Un - √ 68.0 -
FramePred CVPR 18 Un - √ 72.8 -
FramePred* IJCAI 19 Un - √ 73.4 -
Mem-AE ICCV 19 Un - √ 71.2 -
MNAD CVPR 20 Un - √ 70.5 -
VEC ACM MM 20 Un - √ 74.8 -
MLEP IJCAI 19 10% test vids with Video Anno - √ 75.6 -
MLEP IJCAI 19 10% test vids with Frame Anno - √ 76.8 -
Sultani.etl ICME 2020 Weakly (Re-Organized Dataset) C3D-RGB X 86.3 0.15
IBL ICME 2020 Weakly (Re-Organized Dataset) I3D-RGB X 82.5 0.10
GCN-Anomaly CVPR 19 Weakly (Re-Organized Dataset) C3D-RGB √ 76.44 -
GCN-Anomaly CVPR 19 Weakly (Re-Organized Dataset) TSN-Flow √ 84.13 -
GCN-Anomaly CVPR 19 Weakly (Re-Organized Dataset) TSN-RGB √ 84.44 -
AR-Net ICME 20 Weakly (Re-Organized Dataset) I3D-RGB & I3D Flow X 91.24 0.10

Performance Comparision on Avenue

Model Reported on Conference/Journal Supervision Feature End2End AUC(%)
Conv-AE CVPR 16 Un - √ 70.2
Conv-AE* CVPR 18 Un - √ 80.0
ConvLSTM-AE ICME 17 Un - √ 77.0
DeepAppearance ICAIP 17 Un - √ 84.6
Unmasking ICCV 17 Un 3D gradients+VGG conv5 X 80.6
stacked-RNN ICCV 17 Un - √ 81.7
FramePred CVPR 18 Un - √ 85.1
Mem-AE ICCV 19 Un - √ 83.3
Appearance-Motion Correspondence ICCV 19 Un - √ 86.9
FramePred* IJCAI 19 Un - √ 89.2
MNAD CVPR 20 Un - √ 88.5
VEC ACM MM 20 Un - √ 90.2
MLEP IJCAI 19 10% test vids with Video Anno - √ 91.3
MLEP IJCAI 19 10% test vids with Frame Anno - √ 92.8