Papers for Video Anomaly Detection, released codes collection, Performance Comparision.
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
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, Ano-LocalityDownload_link
Background-BiasDownload link
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Project Link
Open-Set
The Datasets belowed are about Traffic Accidents Anticipating in Dashcam videos or Surveillance videos
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CVPR 16
. CodeICCV 2017
. (Explainable VAD)ICCV 2017
. codeICME 2017
.CodeACM MM 17
.ICCV 17
.CVPR 2018
. codeCVPR 2018
. codeACM MM 18
.ICCV 2019
.codeCVPR 2019
.codeCVPR 2019
.ICCV 2019
.codeWACV 2020
.WACV 2020
.CVPR 2020
.codeCVPR 2020
. CVPR 2020
. codeCVPR 2020 Worksop.
CVPR 2020
. codeCVPR 2020 workshop
.ECCV 2020 Spotlight
codeECCV 2020
ACM MM 2020 Oral
codeACCV 2020
ACM MM 2020
ACM MM 2020
AAAI 2021
CVPR 2021
ICCV 2021 Oral
TNNLS 2021
TNNLS 2021
AAAI 2022
AAAI 2022
CVPR 2022
CVPR 2018
codeCVPR 2019
,IJCAI 2019
code.ICIP 19
.BMVC 19
.WACV 2020
.ICME 2020
.codeECCV 2020
ECCV 2020
CVPR 2021
Project PageICCV 2021
CodeIJCAI 2021
TIP 2021
CodeTIP 2021
AAAI 2022
ACM MM 19
.ECCV 2020
code" class="reference-link">1. [Few-Shot]Few-Shot Scene-Adaptive Anomaly Detection ECCV 2020
codeTPAMI 2020
paper.Generally, anomaly detection in recent researches are based on the datasets from pedestrian (likes UCSD, Avenue, ShanghaiTech, etc.), or UCF-Crime (real-world anomaly).
However some focus on specific scene as follows.
CVPR workshop, AI City Challenge series.
Unsupervised Traffic Accident Detection in First-Person Videos, IROS 2019.
When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos. github
As discussed in Issue #12, the reported results below will be Micro-AUC”, if the paper provide
Macro-AUC”, which will be tagged with *
.
Model | Reported on Convference/Journal | Supervised | Feature | Encoder-based | 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 |
ST-Graph | ACM MM 20 | Un | - | √ | X | 72.7 | |
Background-Bias | ACM MM 19 | Fully | NLN RGB | √ | X | 82.0 | - |
CLAWS | ECCV 20 | Weakly | C3D RGB | √ | X | 83.03 | - |
MIST | CVPR 21 | Weakly | I3D RGB | √ | X | 82.30 | 0.13 |
RTFM | ICCV 21 | Weakly | I3D RGB | X | √ | 84.03 | - |
WSAL | TIP 21 | Weakly | I3D RGB | X | √ | 85.38 | - |
CRFD | TIP 21 | Weakly | I3D RGB | X | √ | 84.89 | - |
MSL | AAAI 22 | Weakly | C3D RGB | √ | X | 82.85 | - |
MSL | AAAI 22 | Weakly | I3D RGB | √ | X | 85.30 | - |
MSL | AAAI 22 | Weakly | VideoSwin-RGB | √ | X | 85.62 | - |
GCL | CVPR 22 | Weakly | ResNext | √ | X | 79.84 | - |
GCL | CVPR 22 | Un | ResNext | √ | X | 71.04 | - |
Model | Reported on Conference/Journal | Supervision | Feature | Encoder-based | 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 | - |
ST-Graph | ACM MM 20 | Un | - | √ | 74.7 | - |
CAC | ACM MM 20 | Un | - | √ | 79.3 | |
AMMC | AAAI 21 | Un | - | √ | 73.7 | - |
SSMT | CVPR 21 | Un | - | √ | 82.4 | - |
HF2-VAD | ICCV 21 | Un | - | √ | 76.2 | - |
ROADMAP | TNNLS 21 | Un | - | √ | 76.6 | - |
BDPN | AAAI 22 | Un | - | √ | 78.1 | - |
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 |
CLAWS | ECCV 20 | Weakly (Re-Organized Dataset) | C3D-RGB | √ | 89.67 | |
MIST | CVPR 21 | Weakly (Re-Organized Dataset) | I3D-RGB | √ | 94.83 | 0.05 |
RTFM | ICCV 21 | Weakly (Re-Organized Dataset) | I3D-RGB | X | 97.21 | - |
CRFD | TIP 21 | Weakly (Re-Organized Dataset) | I3D-RGB | X | 97.48 | - |
MSL | AAAI 22 | Weakly (Re-Organized Dataset) | C3D-RGB | X | 94.81 | - |
MSL | AAAI 22 | Weakly (Re-Organized Dataset) | I3D-RGB | X | 96.08 | - |
MSL | AAAI 22 | Weakly (Re-Organized Dataset) | VideoSwin-RGB | X | 97.32 | - |
GCL | CVPR 22 | Weakly (Re-Organized Dataset) | ResNext | X | 86.21 | - |
GCL | CVPR 22 | Un | ResNext | X | 78.93 | - |
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 |
ST-Graph | ACM MM 20 | Un | - | √ | 89.6 |
CAC | ACM MM 20 | Un | - | √ | 87.0 |
AMMC | AAAI 21 | Un | - | √ | 86.6 |
SSMT | CVPR 21 | Un | - | √ | 91.5 |
HF2-VAD | ICCV 21 | Un | - | √ | 91.1 |
ROADMAP | TNNLS 21 | Un | - | √ | 88.3 |
AEP | TNNLS 21 | Un | - | √ | 90.2 |
Causal | AAAI 22 | Un | I3D-RGB | X | 90.3 |
BDPN | AAAI 22 | Un | - | √ | 90.3 |
MLEP | IJCAI 19 | 10% test vids with Video Anno | - | √ | 91.3 |
MLEP | IJCAI 19 | 10% test vids with Frame Anno | - | √ | 92.8 |
Model | Reported on Conference/Journal | Supervision | Feature | Encoder-based | 32 Segments | AP(%) |
---|---|---|---|---|---|---|
Sultani et al. | ECCV 2020 (reported by Wu) | Weakly | I3D-RGB | X | √ | 73.20 |
Wu et al. | ECCV 2020 | Weakly | C3D-RGB | X | X | 67.19 |
Wu et al. | ECCV 2020 | Weakly | I3D-RGB+Audio | X | X | 78.64 |
RTFM | ICCV 2021 | Weakly | I3D-RGB | X | √ | 77.81 |
CRFD | TIP 2021 | Weakly | I3D-RGB | X | √ | 75.90 |
MSL | AAAI 2022 | Weakly | C3D-RGB | X | X | 75.53 |
MSL | AAAI 2022 | Weakly | I3D-RGB | X | X | 78.28 |
MSL | AAAI 2022 | Weakly | VideoSwin-RGB | X | X | 78.59 |