This document presents an intelligent surveillance framework for real-time abnormal behavior recognition from video streams. The framework uses background modeling and Gaussian mixture models to detect foreground regions and track object movements. It then analyzes the detected behaviors to identify any unusual or malicious group activities. The goal is to help human monitors more reliably identify security threats by automating some detection tasks.

1. Intelligent Surveillance for Abnormal Behaviour Recognition
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
Abstract
1
Growing number surveillance camera is challenging the reliability of existing surveillance system which is still relying on
human monitors. This project aims to develop a real-time behaviour recognition framework for identifying unusual group
behaviours from surveillance video stream in order to aid human monitors taking early actions against possible malicious
activities. Incoming video stream passes through several complex processes performed by different components of the
framework for high level recognition.
Background Modelling
2
Movement Trajectory Computation
4
Frame 1
Frame # 75
Frame # 1
Frame 2
..
Frame # 150
Frame # 225
Frame # 300
Input scenes
Frame t
Gaussian Mixture Model (GMM)
for each pixel
P( X t )  iK 1 wi,t  ( X t ,  i,t ,  i,t )

 ( X t , t ,  t ) 
1
(2 )
n/2
||
1/ 2
e
1
 ( X t   t )T
2
 1 ( X t  t )
Behaviour Recognition
5
A pixel model is constructed and updated for each pixel which
maintains a mixture of Gaussian distributions for modelling multimodal distribution caused by moving foregrounds and repetitive
background motions [1-3].
Frame # 75
Frame # 1
Frame # 150
Frame # 225
Frame # 300
Foreground Region Detection
3
6
Performance Evaluation
First Frame
Test Frame
Ideal Result
Actual Result
Background
Model
Current Scene
7
Foreground Region
The Complete System
8
References
[1] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, On Stable Dynamic Background
Generation Technique using Gaussian Mixture Models for Robust Object Detection, to be
appear in IEEE International Conference On Advanced Video and Signal Based Surveillance
(AVSS), New Mexico, USA, 2008.
[2] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, A Hybrid Object Detection
Technique from Dynamic Background Using Gaussian Mixture Models, to be appear in IEEE
International Workshop on Multimedia Signal Processing (MMSP), Cairns, Australia, 2008.
[3] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, Improved Gaussian Mixtures for
Robust Object Detection by Adaptive Multi-Background Generation, to be appear in
International Conference on Pattern Recognition (ICPR), Tampa, Florida, USA, 2008.
The images shown in the header and in the middle of the poster have been taken from http://www.informationliberation.com and
http://www.defenestrator.org/drp/files/surveillance-cameras-400.jpg respectively.