Unsupervised foreground-background segmentation using growing self organizing map in noisy backgrounds

Segmentation of moving objects in an image sequence is one of the most fundamental and crucial steps in visual surveillance applications. This paper proposes a novel and efficient method for detecting moving objects in a noisy background by using a growing self organizing map to construct the codebo...

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Bibliographic Details
Main Authors: Ghasemi, A, Safabakhsh, R
Format: Conference Proceeding
Language:English
Subjects:
Adaptation model
codebook
Computational modeling
Image color analysis
mixture of Gaussians
motion analysis
Neurons
Pixel
Quantization
segmentation
self organizing map
Training
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Summary:Segmentation of moving objects in an image sequence is one of the most fundamental and crucial steps in visual surveillance applications. This paper proposes a novel and efficient method for detecting moving objects in a noisy background by using a growing self organizing map to construct the codebook. The segmentation process distinguishes between those parts of the objects which move on static and dynamic background spaces such as roads and waving trees, respectively. The advantage of the proposed method is creating a small codebook based on the input pattern to model the background which results in less computational complexity and increases the speed of segmentation. We compare the proposed method with three other background subtraction algorithms and show that the proposed method has a higher precision and detection rate in comparison with other methods.
DOI:10.1109/ICCRD.2011.5764031