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Real-world Anomaly Detection in
Surveillance Videos
CVPR 2018
1
펀디멘탈 팀
고형권, 김동희, 김준호, 김창연, 이민경, 송헌, 이재윤
0. Anomaly detection
1. Motivations / Contributions
2. Proposed algorithm
3. Experiments
2
Anomaly detection
3
https://www.youtube.com/watch?v=mbbmeboRurI
Anomaly detection
• Use cases
• Log Anomaly Detection
• Fraud Detection
• credit cards
• Medical Anomaly Detection
• MRI image
• Industrial Anomaly
Detection
• robot arms
• Video Surveillance
4
https://www.youtube.com/watch?v=qS1iPiSW3RE
0. Anomaly detection
1. Motivations / Contributions
2. Proposed algorithm
3. Experiments
5
Motivation
• It is difficult to list all possible anomalous events.
• The boundary between normal and anomalous behaviors is often
ambiguous.
6
Motivation
• Examples
7
http://www.svcl.ucsd.edu/projects/anomaly/dataset.html
https://www.merl.com/demos/video-anomaly-detection
Motivation
• The environment captured by surveillance cameras can change
drastically over the time (e.g., at different times of a day), these
approaches produce high false alarm rates for different normal
behaviors.
• The same behavior could be a normal or an anomalous behavior under
different conditions
8
Motivation
• Examples
9
https://www.kaggle.com/aalborguniversity/aau-rainsnow
How to solve?
• Anomaly detection should be done with minimum
supervision.
10
An anomaly detection algorithm
using weakly labeled training videos
How to solve?
• Previous approach
11
• This paper
0 0 0 0 1
Video1: 1 Video2: 0
http://www.svcl.ucsd.edu/projects/anomaly/dataset.html
Contributions
• An anomaly detection algorithm using weakly labeled training videos
• A new large-scale video anomaly detection dataset consisting of 1900
real-world surveillance videos of 13 different anomalous events and
normal activities captured by surveillance cameras.
• Superior performance as compared to the SOTA anomaly detection
approaches (2018)
12
Any questions?
13
0. Anomaly detection
1. Motivations / Contributions
2. Proposed algorithm
3. Experiments
14
Multiple Instance Learning [1, 2]
• Precise temporal locations of anomalous events in
videos are unknown.
• Annotating them are laborious.
• Instead of receiving a set of instances which are
individually labeled, the learner receives a set of
labeled bags, each containing many instances.
15
[1] T. G. Dietterich, R. H. Lathrop, and T. Lozano-P´erez. Solving the multiple instance problem
with axis-parallel rectangles. Artificial Intelligence, 89(1):31–71, 1997.
[2] S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-
instance learning. In NIPS, pages 577–584, Cambridge, MA, USA, 2002. MIT Press.
https://en.wikipedia.org/wiki/Multiple_instance_learning
Multiple Instance Learning
16
Multiple Instance Learning
• A positive bag 𝐵𝑝 = {𝑝1, 𝑝2, … , 𝑝𝑚}
• A negative bag 𝐵𝑛 = {𝑛1, 𝑛2, … , 𝑛𝑚}
17
Image from original paper
How to score each of them?
18
Image from original paper
Deep MIL Ranking Model
• Used C3D [1] pretrained model
19
[1] D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri.
Learning spatiotemporal features with 3d convolutional networks. In
ICCV, 2015.
Deep MIL Ranking Model
20
Deep MIL Ranking Model
• Limitation: it ignores the underlying temporal structure of the
anomalous video
• anomaly often occurs only for a short time.
• since the video is a sequence of segments, the anomaly score should vary
smoothly between video segments.
• temporal smoothness between anomaly scores of temporally adjacent
video segments by minimizing the difference of scores for adjacent
video segments.
21
Deep MIL Ranking Model
22
𝜆1 = 𝜆2 = 8 ∗ 10−5
(the best performance)
Complete objective function
23
𝜆3 = 0.01 (the best performance)
Overall pipeline
24
Image from original paper
Any questions?
25
0. Anomaly detection
1. Motivations / Contributions
2. Proposed algorithm
3. Experiments
26
Previous datasets
• UMN dataset
• Anomaly: only running action
• UCSD ped1, ped2
• Avenue
• Subway exit, entrance
• BOSS
• Abnormal crowd
27
Proposed dataset
• long untrimmed surveillance videos which cover 13 real-world
anomalies, including Abuse, Arrest, Arson, Assault, Accident,
Burglary, Explosion, Fighting, Robbery, Shooting, Stealing,
Shoplifting, and Vandalism.
28
https://www.crcv.ucf.edu/research/real-world-anomaly-detection-in-
surveillance-videos/
UCF-crime dataset
29
Datasets (table)
30
Baseline methods
• Lu et al. ([1], dictionary based approach)
• Hasan et al. ([2], a fully convolutional feedforward deep auto-encoder
based approach)
• Binary SVM classifier
31
[1] C. Lu, J. Shi, and J. Jia. Abnormal event detection at 150 fps in
matlab. In ICCV, 2013.
[2] M. Hasan, J. Choi, J. Neumann, A. K. Roy-Chowdhury, and L. S. Davis.
Learning temporal regularity in video sequences. In CVPR, June 2016.
Evaluation metrics
• ROC curve
• AUC (area under curve)
32
Quantitative results
33
Image from original paper
Quantitative results
34
Image from original paper
Qualitative results
35
Image from original paper
Conclusion
• Proposed a deep learning approach to detect real-world anomalies in
surveillance videos
• Introduced a new large-scale anomaly dataset consisting of a variety of
real-world anomalies
• Outperformed previous methods
36
Any questions?
37

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