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Poster: Monash Research Month 2009
1. Intelligent Security
Application for Unusual Event Detection
Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul
Gippsland School of Information Technology, Monash University, Victoria 3842, Australia
Email: {Mahfuzul.Haque, Manzur.Murshed, Manoranjan.Paul}@infotech.monash.edu.au
1
Abstract
The goal of this research project is to develop intelligent software that can see and detect unusual events from
surveillance video stream. As the growing number of security camera is challenging the reliability of existing security
systems which are still relying on human monitors, this project exploits computer vision and artificial intelligence for
developing intelligent security application that can aid human monitors taking early actions against malicious events.
2
Motivation
5
Event Detection
Frame # 1
6
Frame # 75
Frame # 150
Frame # 225
Frame # 300
Performance Evaluation
First Frame
Test Frame
Ideal Result
Actual Result
Image source: http://www.alert-sec.co.uk/Images/control_room.jpg
Security control room: too many cameras, too many screens!
3
Foreground Region Detection
Background
Model
Current Scene
Foreground Region
First, active foreground regions are separated using an adaptive model of
the scene background [1][2], and then scene level features are extracted
from those regions.
4
7
The Application
8
References
Temporal Feature Analysis
Total Foreground Area (%)
Frame # 1
Frame # 75
Frame # 150
Frame # 225
Frame # 300
Total 270 temporal features are computed
from the time varying scene level features.
Finally, top ranked features are mapped to
high level event models.
Observed Data
Smoothed Data
Time
[1] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, On Stable Dynamic Background
Generation Technique using Gaussian Mixture Models for Robust Object Detection, IEEE
International Conference On Advanced Video and Signal Based Surveillance (AVSS), New Mexico,
USA, 2008.
[2] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, Improved Gaussian Mixtures for
Robust Object Detection by Adaptive Multi-Background Generation, International Conference
on Pattern Recognition (ICPR), Tampa, Florida, USA, 2008.
![Intelligent Security
Application for Unusual Event Detection
Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul
Gippsland School of Information Technology, Monash University, Victoria 3842, Australia
Email: {Mahfuzul.Haque, Manzur.Murshed, Manoranjan.Paul}@infotech.monash.edu.au
1
Abstract
The goal of this research project is to develop intelligent software that can see and detect unusual events from
surveillance video stream. As the growing number of security camera is challenging the reliability of existing security
systems which are still relying on human monitors, this project exploits computer vision and artificial intelligence for
developing intelligent security application that can aid human monitors taking early actions against malicious events.
2
Motivation
5
Event Detection
Frame # 1
6
Frame # 75
Frame # 150
Frame # 225
Frame # 300
Performance Evaluation
First Frame
Test Frame
Ideal Result
Actual Result
Image source: http://www.alert-sec.co.uk/Images/control_room.jpg
Security control room: too many cameras, too many screens!
3
Foreground Region Detection
Background
Model
Current Scene
Foreground Region
First, active foreground regions are separated using an adaptive model of
the scene background [1][2], and then scene level features are extracted
from those regions.
4
7
The Application
8
References
Temporal Feature Analysis
Total Foreground Area (%)
Frame # 1
Frame # 75
Frame # 150
Frame # 225
Frame # 300
Total 270 temporal features are computed
from the time varying scene level features.
Finally, top ranked features are mapped to
high level event models.
Observed Data
Smoothed Data
Time
[1] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, On Stable Dynamic Background
Generation Technique using Gaussian Mixture Models for Robust Object Detection, IEEE
International Conference On Advanced Video and Signal Based Surveillance (AVSS), New Mexico,
USA, 2008.
[2] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, Improved Gaussian Mixtures for
Robust Object Detection by Adaptive Multi-Background Generation, International Conference
on Pattern Recognition (ICPR), Tampa, Florida, USA, 2008.](https://image.slidesharecdn.com/poster-monash-research-month-2009-131229013009-phpapp02/85/Poster-Monash-Research-Month-2009-1-320.jpg)
