Foreground Detection by Competitive Learning for Varying Input Distributions

One of the most important challenges in computer vision applications is the background modeling, especially when the background is dynamic and the input distribution might not be stationary, i.e. the distribution of the input data could change with time (e.g. changing illuminations, waving trees, wa...

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Bibliographic Details
Published in:International journal of neural systems 2018-06, Vol.28 (5), p.1750056
Main Authors: López-Rubio, Ezequiel, Molina-Cabello, Miguel A., Luque-Baena, Rafael Marcos, Domínguez, Enrique
Format: Article
Language:English
Subjects:
Communication channels
Computer vision
Neural networks
Unsupervised learning
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Summary:One of the most important challenges in computer vision applications is the background modeling, especially when the background is dynamic and the input distribution might not be stationary, i.e. the distribution of the input data could change with time (e.g. changing illuminations, waving trees, water, etc.). In this work, an unsupervised learning neural network is proposed which is able to cope with progressive changes in the input distribution. It is based on a dual learning mechanism which manages the changes of the input distribution separately from the cluster detection. The proposal is adequate for scenes where the background varies slowly. The performance of the method is tested against several state-of-the-art foreground detectors both quantitatively and qualitatively, with favorable results.
ISSN:0129-0657
1793-6462
DOI:10.1142/S0129065717500563