poster: monash research month 2008
Post on 24-May-2015
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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
1 Abstract
2 Background Modelling
3 Foreground Region Detection
4 Movement Trajectory Computation
5 Behaviour Recognition
6 Performance Evaluation
8 References
7 The Complete System
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.
[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.
First Frame Test Frame Ideal Result Actual Result
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Gaussian Mixture Model (GMM)
for each pixel
Input scenes
A pixel model is constructed and updated for each pixel which
maintains a mixture of Gaussian distributions for modelling multi-
modal distribution caused by moving foregrounds and repetitive
background motions [1-3].
Background
Model
Current Scene Foreground Region
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.
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