Background modeling using Object-based Selective Updating and Correntropy adaptation
作者：
Highlights：
• We propose a background modeling method to deal with non-stationary conditions.
• The object-based updating strategy allows to work with SFO and RFO.
• Non-Gaussian pixel dynamics are modeled using a Correntropy cost function.
• Analyzing the regions movement direction avoids tracking background objects.
• Object-based updating strategies improve the performance for indoor scenarios.
摘要
•We propose a background modeling method to deal with non-stationary conditions.•The object-based updating strategy allows to work with SFO and RFO.•Non-Gaussian pixel dynamics are modeled using a Correntropy cost function.•Analyzing the regions movement direction avoids tracking background objects.•Object-based updating strategies improve the performance for indoor scenarios.
论文关键词：Background modeling,Learning rate,Correntropy-based adaptation,Moving object detection
论文评审过程：Received 23 July 2014, Revised 17 September 2015, Accepted 6 November 2015, Available online 18 December 2015, Version of Record 5 January 2016.
论文官网地址：https://doi.org/10.1016/j.imavis.2015.11.006
