Background modeling using Object-based Selective Updating and Correntropy adaptation
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- 作者：A.M. Á ; lvarez-Meza ; amalavarezme@unal.edu.co" class="auth_mail" title="E-mail the corresponding author ; S. Molina-Giraldo smolinag@unal.edu.co" class="auth_mail" title="E-mail the corresponding author ; G. Castellanos-Dominguez cgcastellanosd@unal.edu.co" class="auth_mail" title="E-mail the corresponding author
- 关键词：Background modeling ; Learning rate ; Correntropy-based adaptation ; Moving object detection
- 刊名：Image and Vision Computing
- 出版年：2016
- 出版时间：January 2016
- 年：2016
- 卷：45
- 期：Complete
- 页码：22-36
- 全文大小：3153 K
文摘
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We propose a background modeling method to deal with non-stationary conditions.
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The object-based updating strategy allows to work with SFO and RFO.
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Non-Gaussian pixel dynamics are modeled using a Correntropy cost function.
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Analyzing the regions movement direction avoids tracking background objects.
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Object-based updating strategies improve the performance for indoor scenarios.