Panic-driven Event Detection from Surveillance
Video Stream without Track and Motion Features

Mahfuzul Haque and Manzur M...
Presentation Outline
• Introduction
– Area
– Problem
– Objective

• Event Detection
• The Idea
– Why not track or motion f...
Research Area
Dynamic Scene Understanding

Stage 1

Video Stream






Stage 2

…

Real-time Processing

Event Detecti...
The Problem
Dynamic Scene Understanding

Stage 1

Video Stream

Stage 2

…

Real-time Processing

 Scene specific tuning
...
Research Objectives
Dynamic Scene Understanding

Stage 1

Video Stream

Stage 2

…

Real-time Processing

Event
Model
Anal...
Event Detection

time





Specific types of events vs. abnormality
An event persists for a certain duration of time
T...
The Idea
f1
f2
f3
.
.
.

time
Frame-level
Features






Event
Model

fn
Temporal
Features

Classifier

Event detectio...
The Idea
Motion based approaches

Tracking based approaches

 Key points detection
 Point matching in successive frames
...
The Idea
f1
f2
f3
.
.
.

time
Frame-level
Features

Event
Model

fn
Temporal
Features

Classifier

Summary
 Object based ...
The Proposed Method
Architecture
Foreground
Detector

Frame-level
Feature Extractor

Temporal
Feature Extractor

Event
Mod...
The Proposed Method
Frame-level features










Blob Area (BA)
Filling Ratio (FR)
Aspect Ratio (AR)
Bounding Bo...
The Proposed Method
Temporal features
2
1

4
3

6
5

Frame #

 Overlapping sliding window
 Temporal order
 Speed of var...
The Proposed Method
Blob Count (BC), Blob Area (BA)
The Proposed Method
Blob Distance (BD)
The Proposed Method
Aspect Ratio (AR)
The Proposed Method
Top five features for four different events

Feature ranking using absolute value criteria of two samp...
Experimental Results
Specific Event Detection
•
•
•
•
•
•
•

Four different events: meet, split, runaway, and fight
CAVIAR...
Experimental Results
Experimental Results
Specific Event Detection

Actual

Predicted

Severity
Experimental Results
Abnormal Event Detection
•
•
•
•

University of Minnesota crowd dataset (UMN dataset)
The Runaway eve...
Experimental Results
Abnormal Event Detection (UMN-9)
Experimental Results
Abnormal Event Detection (UMN-10)
Experimental Results
Abnormal Event Detection (UMN-01)
Experimental Results
Abnormal Event Detection (UMN-07)
Experimental Results
Performance Comparison

Method

AUC

Our Method

0.89

Pure Optical Flow [1]

0.84

[1] R. Mehran, A....
Publication
Mahfuzul Haque and Manzur Murshed, “Panic-driven Event Detection
From Surveillance Video Stream without Track ...
Thanks!

Q&A
Mahfuzul.Haque@infotech.monash.edu.au
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Talk 2010-monash-seminar-panic-driven-event-detection

  1. 1. Panic-driven Event Detection from Surveillance Video Stream without Track and Motion Features Mahfuzul Haque and Manzur Murshed
  2. 2. Presentation Outline • Introduction – Area – Problem – Objective • Event Detection • The Idea – Why not track or motion features? • The Proposed Method • Experimental Results • Q&A
  3. 3. Research Area Dynamic Scene Understanding Stage 1 Video Stream     Stage 2 … Real-time Processing Event Detection Action / Activity Recognition Behaviour Recognition Behaviour Profiling Event Model Analytics  Intelligent Video Surveillance  Automated Alert  Smart Monitoring  Context-aware Environments
  4. 4. The Problem Dynamic Scene Understanding Stage 1 Video Stream Stage 2 … Real-time Processing  Scene specific tuning  Availability of training data Large Surveillance Network  Thousands of video feeds  Ad-hoc remote surveillance  Dynamic scene variations Event Model Analytics How to develop a generic scene understanding framework that would reliably work on a wider range of scenarios?
  5. 5. Research Objectives Dynamic Scene Understanding Stage 1 Video Stream Stage 2 … Real-time Processing Event Model Analytics  A generic scene understanding framework  Developing the building blocks for the essential processing stages  Scope:  Panic-driven abnormality detection  A fixed set of specific events
  6. 6. Event Detection time     Specific types of events vs. abnormality An event persists for a certain duration of time The duration is variable Event characteristics of the same event  Variable in the same environment How to identify the generic  Variable from one scene to other characteristics of an event?
  7. 7. The Idea f1 f2 f3 . . . time Frame-level Features     Event Model fn Temporal Features Classifier Event detection as temporal data classification problem A distinct set of temporal features can characterise an event Which/how frame-level features are extracted? How the observed frame-level features are transformed in temporal-features?
  8. 8. The Idea Motion based approaches Tracking based approaches  Key points detection  Point matching in successive frames  Flow vectors: position, direction, speed  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) Proposed generic approach  Object detection  Global frame-level descriptor: independent of scene characteristics Xiang et al. (IJCV 2006)  No Inter-frame association  Independent frame-level features => temporal features considering speed and temporal order
  9. 9. The Idea f1 f2 f3 . . . time Frame-level Features Event Model fn Temporal Features Classifier Summary  Object based approach  Independent frame-level features– no object / position specific information, no spatial association  Frame-level features are transformed into temporal features considering speed and temporal order  Supposed to be more context invariant
  10. 10. The Proposed Method Architecture Foreground Detector Frame-level Feature Extractor Temporal Feature Extractor Event Models Model Training Frame-level Feature Extraction (30 features) Background Subtraction Labelled frames Temporal Feature Extraction (270 features) Feature Ranking and Selection Event Model Training Foreground blobs Real-time Execution Selective Frame-level Feature Extraction Background Subtraction Incoming frames Foreground blobs Selective Temporal Feature Extraction Trained Event Models Detection Results
  11. 11. The Proposed Method 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)
  12. 12. The Proposed Method Temporal features 2 1 4 3 6 5 Frame #  Overlapping sliding window  Temporal order  Speed of variation
  13. 13. The Proposed Method Blob Count (BC), Blob Area (BA)
  14. 14. The Proposed Method Blob Distance (BD)
  15. 15. The Proposed Method Aspect Ratio (AR)
  16. 16. The Proposed Method Top five features for four different events Feature ranking using absolute value criteria of two sample t-test, based on pooled variance estimate.
  17. 17. Experimental Results Specific Event Detection • • • • • • • 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
  18. 18. Experimental Results
  19. 19. Experimental Results Specific Event Detection Actual Predicted Severity
  20. 20. Experimental Results Abnormal Event Detection • • • • University of Minnesota crowd dataset (UMN dataset) The Runaway event model No additional training or tuning Three different sites
  21. 21. Experimental Results Abnormal Event Detection (UMN-9)
  22. 22. Experimental Results Abnormal Event Detection (UMN-10)
  23. 23. Experimental Results Abnormal Event Detection (UMN-01)
  24. 24. Experimental Results Abnormal Event Detection (UMN-07)
  25. 25. Experimental Results Performance Comparison Method AUC Our 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, 20–25 June 2009, pp. 935–942.
  26. 26. Publication Mahfuzul Haque and Manzur Murshed, “Panic-driven Event Detection From Surveillance Video Stream without Track and Motion Features,” IEEE International Conference on Multimedia & Expo (ICME), 2010.
  27. 27. Thanks! Q&A Mahfuzul.Haque@infotech.monash.edu.au

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