Talk 2012-icmew-event

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

December 30, 2013 Abnormal Event Detection in Unseen Scenarios 2

Event Detection for Public Safety


Mob Violence

Crowding

Sudden Group Formation

Sudden Group Deformation

Shooting

Panic Driven Behaviours

Event Detection


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

time

Challenges


Challenges


Abnormal Event Detection

Supervised Unsupervised Semi-supervised

No Event Model Explicit Event Model

Clustering of observed patterns,

Database of spatiotemporal patches

Normal event modelling:

manual labelling,

Abnormal event modelling:

unsupervised adaptation

Manual Labelling,

Prior assumption of well

define event classes

More Recent Approach

Mixture of Dynamic Bayesian Networks

Challenges


Build Event Model Once

Operate Everywhere

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

Proposed Approach


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

time

f1

f2

f3

.

fn

Event

Model

Frame-level Features Temporal Features Classifier

Proposed Approach


Background

Subtraction

Frame-level

Feature Extraction

(30 features)

Temporal

Feature Extraction

(270 features)Labelled frames Foreground blobs

Feature Ranking

and Selection

Event Model

Training

Model Training (offline)

Event

Models

Foreground

Detector

Frame-level

Feature Extractor

Temporal

Feature Extractor

Processes

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)

Blob Statistical Analysis


Blob Count (BC), Blob Area (BA)

Blob Statistical Analysis Blob Distance (BD)

December 30, 2013 13 Abnormal Event Detection in Unseen Scenarios

Blob Statistical Analysis

Aspect Ratio (AR)


Feature Extraction


Frame #

1

2

3

4

5

6

Temporal features

Overlapping sliding window

Temporal order

Speed of variation

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.


Proposed Approach


Background

Subtraction

Selective

Frame-level

Feature Extraction

Selective

Temporal

Feature ExtractionIncoming frames Foreground blobs

Trained

Event Models

Detection

Results

Background

Subtraction

Frame-level

Feature Extraction

(30 features)

Temporal

Feature Extraction

(270 features)Labelled frames Foreground blobs

Feature Ranking

and Selection

Event Model

Training

Model Training (offline)

Event Detection in the Operating Environment

Summary and Discussion


Key points detection

Point matching in successive frames

Flow vectors: position, direction, speed

Motion based approaches Tracking based approaches

Object detection

Object matching in successive frames

Trajectories: object paths

Inter-frame association

Context specific information

Event models are not generic

Common characteristics

Foreground blob detection

Global frame-level descriptor based on

blob statistical analysis, independent

of scene characteristics

Proposed approach No Inter-frame association

Independent frame-level features =>

temporal features considering speed

and temporal order

Hu et al. (ICPR 2008) Xiang et al. (IJCV 2006)

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

Experiments


Event Models

Unseen Scenarios in

Known Context

Unseen Scenarios in

Unknown Context

Greenfield Outdoor Corridor

Experiments


Experiments


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

Experiments


Abnormal Event Detection (UMN-9)

Experiments



Experiments



Experiments



Experiments


Experiments


[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.

Method AUC

Proposed Method 0.89

Pure Optical Flow [1] 0.84

Q&A
