poster: monash research month 2007
TRANSCRIPT
A Robust Moving Object Detection Method for Visual Surveillance 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
As the importance of human security is increasing day by day, the number of surveillance camera is also growing. But the role of
next generation surveillance system is not only capturing video data from different locations and using them for post-incident
analysis but also providing support for automatic tracking and early recognition of suspicious activities before any undesirable event
happens. This research attempts to propose a robust moving object detection method for automatic visual surveillance .
Detection results
Conclusion
The object detection results and the comparative analysis show that the
proposed method performed better than existing methods.
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Gaussian Mixture Model (GMM)
for each pixel
Input frames
First Frame Test Frame Ideal Result Actual Result
Comparative analysis of the experimental results
The technique behind the scene
Moving Object Detection Toolkit
The image shown in the header has been taken from http://www.informationliberation.com
The test sequences have been taken from the PETS database (http://www.cvg.rdg.ac.uk/slides/pets.html)
Tool for analysing underlying statistical model
Note: Fewer false classifications refer preferred detection method.
A pixel model is constructed and updated for each pixel which maintains a
Mixture of Gaussian distributions to address the multi-modal distribution caused
by illumination variations or a repetitive background motions due to moving
leaves on a tree or moving clouds.
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Test Sequence
Fals
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lassifi
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thousands)
GMM (Stauffer)
GMM (Lee)
Proposed Method