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SOTA Abnormal Event Detection benchmarks and papers with code | Wizwand
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Abnormal Event Detection
Benchmarks
Dataset Name
SOTA Method
Dataset Name
SOTA Method
Metric
Trend
Results
Last Updated
UCSD Ped2
SSMT
AUC
99.8
150
17d ago
UCSD Ped2 (test)
3D CNN Multi-task Learning
AUC
99.8
146
1mo ago
Avenue (test)
Method [54] + [27]
RBDC
66.04
37
1mo ago
UCSD Ped1 (test)
AM-GAN
Frame AUC
97.4
33
1mo ago
UCSD Ped1
Proposed Method
AUC
0.974
28
1mo ago
Avenue dataset (test)
object-centric convolutional auto-encoders
Frame AUC
90.4
27
1mo ago
UMN dataset
Sun et al.
Frame AUC (All Scenes)
99.7
25
1mo ago
Subway Exit
Method [8]
Correct Detections
19
14
1mo ago
UMN Unusual Crowd Activity (test)
Sabokrou et al.
Frame-level AUC
99.6
13
1mo ago
UCSD Ped2 v1 (test)
Proposed method (G_theta and D_phi)
AUC
96.5
12
1mo ago
UCSD Ped1 v1 (test)
Optical flow-GAN
AUC
97.4
12
1mo ago
Subway Entrance Old labels (test)
Hasan et al.
AUC
94.3
11
1mo ago
Subway Entrance
Conv-AE
True Positives
61
11
1mo ago
Subway Exit Old labels (test)
Ionescu et al. [7]
AUC
95.1
9
1mo ago
Subway
Hasan et al.
Frame AUC (Entrance gate)
94.3
7
1mo ago
Subway Entrance gate
Cong et al.
Frame AUC
80
5
1mo ago
Subway Entrance New labels (test)
Background-Agnostic Framework
AUC
92.2
3
1mo ago
Subway Exit New labels (test)
Background-Agnostic Framework
AUC
93.7
3
1mo ago
CUHK Avenue Old and New
Conv-AE
Correct Detection
45
3
1mo ago
Avenue 17
NNC
Frame AUC
91.1
2
1mo ago
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