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Information Technology

Abnormal Event Detection in
Unseen Scenarios
Mahfuzul Haque and Manzur Murshed
Outline
 Event Detection for Public Safety
 Challenges
 Proposed Approach

 Experiments
 Summary

 Q&A

Abnormal Eve...
Event Detection for Public Safety
 Mob Violence
 Crowding
 Sudden Group Formation
 Sudden Group Deformation
 Shooting...
Event Detection

time





Specific event (e.g., run) detection vs. abnormality detection
An event persists for a cert...
Challenges

Abnormal Event Detection in Unseen Scenarios

December 30, 2013

5
Challenges
Abnormal Event Detection

Supervised

Unsupervised

Manual Labelling,
Prior assumption of well
define event cla...
Challenges

Build Event Model Once

Operate Everywhere
Abnormal Event Detection in Unseen Scenarios

December 30, 2013

7
Proposed Approach
Build
•
•
•
•

Targeted Events
Extensive Feature Extraction
Feature Selection/Ranking
Supervised

Operat...
Proposed Approach
f1
f2

Event

f3

Model

.

time
Frame-level Features

fn
Temporal Features

Classifier

 Event detecti...
Proposed Approach
Processes
Foreground
Detector

Frame-level
Feature Extractor

Temporal
Feature Extractor

Event
Models

...
Blob-Statistical Analysis
Frame-level features










Blob Area (BA)
Filling Ratio (FR)
Aspect Ratio (AR)
Bound...
Blob Statistical Analysis
Blob Count (BC), Blob Area (BA)

Abnormal Event Detection in Unseen Scenarios

December 30, 2013...
Blob Statistical Analysis
Blob Distance (BD)

Abnormal Event Detection in Unseen Scenarios

December 30, 2013

13
Blob Statistical Analysis
Aspect Ratio (AR)

Abnormal Event Detection in Unseen Scenarios

December 30, 2013

14
Feature Extraction
Temporal features
2
1

4
3

6
5

Frame #

 Overlapping sliding window
 Temporal order
 Speed of vari...
Feature Extraction
Top five features for four different events

Feature ranking using absolute value criteria of two sampl...
Proposed Approach
Model Training (offline)
Frame-level
Feature Extraction
(30 features)

Background
Subtraction
Labelled f...
Summary and Discussion
Motion based approaches
 Key points detection
 Point matching in successive frames
 Flow vectors...
Experiments

Model Training
•
•
•
•
•
•
•

Four different events: meet, split, runaway, and fight
CAVIAR dataset with labe...
Experiments
Event Models

Unseen Scenarios in
Known Context

Unseen Scenarios in
Unknown Context

Greenfield

Outdoor

Abn...
Experiments

Abnormal Event Detection in Unseen Scenarios

December 30, 2013

21
Experiments

Abnormal Event Detection in Unseen Scenarios

December 30, 2013

22
Experiments
• Abnormal event detection in unseen scenarios in
unknown context
• University of Minnesota crowd dataset (UMN...
Experiments
Abnormal Event Detection (UMN-9)

Abnormal Event Detection in Unseen Scenarios

December 30, 2013

24
Experiments
Abnormal Event Detection (UMN-10)

Abnormal Event Detection in Unseen Scenarios

December 30, 2013

25
Experiments
Abnormal Event Detection (UMN-01)

Abnormal Event Detection in Unseen Scenarios

December 30, 2013

26
Experiments
Abnormal Event Detection (UMN-07)

Abnormal Event Detection in Unseen Scenarios

December 30, 2013

27
Experiments

Abnormal Event Detection in Unseen Scenarios

December 30, 2013

28
Experiments

Method

AUC

Proposed Method

0.89

Pure Optical Flow [1]

0.84

[1] R. Mehran, A. Oyama, and M. Shah, “Abnor...
Q&A

Abnormal Event Detection in Unseen Scenarios

December 30, 2013

30
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Transcript of "Talk 2012-icmew-event"

  1. 1. Information Technology Abnormal Event Detection in Unseen Scenarios Mahfuzul Haque and Manzur Murshed
  2. 2. Outline  Event Detection for Public Safety  Challenges  Proposed Approach  Experiments  Summary  Q&A Abnormal Event Detection in Unseen Scenarios December 30, 2013 2
  3. 3. Event Detection for Public Safety  Mob Violence  Crowding  Sudden Group Formation  Sudden Group Deformation  Shooting  Panic Driven Behaviours Abnormal Event Detection in Unseen Scenarios December 30, 2013 3
  4. 4. Event Detection time     Specific event (e.g., run) detection vs. abnormality detection An event persists for a certain duration of time The duration is variable The characteristics of the same event is  variable in the same environment  variable from one scene to other Abnormal Event Detection in Unseen Scenarios December 30, 2013 4
  5. 5. Challenges Abnormal Event Detection in Unseen Scenarios December 30, 2013 5
  6. 6. Challenges Abnormal Event Detection Supervised Unsupervised Manual Labelling, Prior assumption of well define event classes No Event Model Clustering of observed patterns, Database of spatiotemporal patches Semi-supervised Normal event modelling: manual labelling, Abnormal event modelling: unsupervised adaptation Explicit Event Model More Recent Approach Mixture of Dynamic Bayesian Networks Abnormal Event Detection in Unseen Scenarios December 30, 2013 6
  7. 7. Challenges Build Event Model Once Operate Everywhere Abnormal Event Detection in Unseen Scenarios December 30, 2013 7
  8. 8. Proposed Approach Build • • • • Targeted Events Extensive Feature Extraction Feature Selection/Ranking Supervised Operation • • • • • No additional training No parameter tuning Selected feature extraction No feature ranking Real-time detection Abnormal Event Detection in Unseen Scenarios December 30, 2013 8
  9. 9. Proposed Approach f1 f2 Event f3 Model . time Frame-level Features fn Temporal Features Classifier  Event detection as temporal data classification problem  A distinct set of temporal features can characterise an event  Independent frame-level features extracted using blob statistical analysis; no object / position specific information, no spatial association  Frame-level features are transformed into temporal features considering speed and temporal order Abnormal Event Detection in Unseen Scenarios December 30, 2013 9
  10. 10. Proposed Approach Processes Foreground Detector Frame-level Feature Extractor Temporal Feature Extractor Event Models Model Training (offline) Frame-level Feature Extraction (30 features) Background Subtraction Labelled frames Temporal Feature Extraction (270 features) Feature Ranking and Selection Event Model Training Foreground blobs Abnormal Event Detection in Unseen Scenarios December 30, 2013 10
  11. 11. Blob-Statistical Analysis Frame-level features         Blob Area (BA) Filling Ratio (FR) Aspect Ratio (AR) Bounding Box Area (BBA) Bounding box Width (BBW) Bounding box Height (BBH) Blob Count (BC) Blob Distance (BD) Abnormal Event Detection in Unseen Scenarios December 30, 2013 11
  12. 12. Blob Statistical Analysis Blob Count (BC), Blob Area (BA) Abnormal Event Detection in Unseen Scenarios December 30, 2013 12
  13. 13. Blob Statistical Analysis Blob Distance (BD) Abnormal Event Detection in Unseen Scenarios December 30, 2013 13
  14. 14. Blob Statistical Analysis Aspect Ratio (AR) Abnormal Event Detection in Unseen Scenarios December 30, 2013 14
  15. 15. Feature Extraction Temporal features 2 1 4 3 6 5 Frame #  Overlapping sliding window  Temporal order  Speed of variation Abnormal Event Detection in Unseen Scenarios December 30, 2013 15
  16. 16. Feature Extraction Top five features for four different events Feature ranking using absolute value criteria of two sample t-test, based on pooled variance estimate. Abnormal Event Detection in Unseen Scenarios December 30, 2013 16
  17. 17. Proposed Approach Model Training (offline) Frame-level Feature Extraction (30 features) Background Subtraction Labelled frames Temporal Feature Extraction (270 features) Feature Ranking and Selection Event Model Training Foreground blobs Event Detection in the Operating Environment Selective Frame-level Feature Extraction Background Subtraction Incoming frames Selective Temporal Feature Extraction Trained Event Models Detection Results Foreground blobs Abnormal Event Detection in Unseen Scenarios December 30, 2013 17
  18. 18. Summary and Discussion Motion based approaches  Key points detection  Point matching in successive frames  Flow vectors: position, direction, speed Tracking based approaches  Object detection  Object matching in successive frames  Trajectories: object paths Common characteristics  Inter-frame association  Context specific information  Event models are not generic Hu et al. (ICPR 2008) Xiang et al. (IJCV 2006) Proposed approach  No Inter-frame association  Foreground blob detection  Independent frame-level features =>  Global frame-level descriptor based on temporal features considering speed blob statistical analysis, independent and temporal order of scene characteristics Abnormal Event Detection in Unseen Scenarios December 30, 2013 18
  19. 19. Experiments Model Training • • • • • • • Four different events: meet, split, runaway, and fight CAVIAR dataset with labelled frames 80% of the test frames for model training 100 iterations of 10-fold cross validation Remaining 20% of the test frames for testing SVM classifier as event models Separate model for each event Abnormal Event Detection in Unseen Scenarios December 30, 2013 19
  20. 20. Experiments Event Models Unseen Scenarios in Known Context Unseen Scenarios in Unknown Context Greenfield Outdoor Abnormal Event Detection in Unseen Scenarios Corridor December 30, 2013 20
  21. 21. Experiments Abnormal Event Detection in Unseen Scenarios December 30, 2013 21
  22. 22. Experiments Abnormal Event Detection in Unseen Scenarios December 30, 2013 22
  23. 23. Experiments • Abnormal event detection in unseen scenarios in unknown context • University of Minnesota crowd dataset (UMN dataset) • The Runaway event model • No additional training or tuning • Three different sites Greenfield Outdoor Corridor Abnormal Event Detection in Unseen Scenarios December 30, 2013 23
  24. 24. Experiments Abnormal Event Detection (UMN-9) Abnormal Event Detection in Unseen Scenarios December 30, 2013 24
  25. 25. Experiments Abnormal Event Detection (UMN-10) Abnormal Event Detection in Unseen Scenarios December 30, 2013 25
  26. 26. Experiments Abnormal Event Detection (UMN-01) Abnormal Event Detection in Unseen Scenarios December 30, 2013 26
  27. 27. Experiments Abnormal Event Detection (UMN-07) Abnormal Event Detection in Unseen Scenarios December 30, 2013 27
  28. 28. Experiments Abnormal Event Detection in Unseen Scenarios December 30, 2013 28
  29. 29. Experiments Method AUC Proposed Method 0.89 Pure Optical Flow [1] 0.84 [1] R. Mehran, A. Oyama, and M. Shah, “Abnormal crowd behavior detection using social force model,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition CVPR 2009, Event Detection2009, pp.Scenarios Abnormal 20–25 June in Unseen 935–942. December 30, 2013 29
  30. 30. Q&A Abnormal Event Detection in Unseen Scenarios December 30, 2013 30
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