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Multi-ratio fusion change detection

机译:多比例融合变化检测

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摘要

In this paper three ratio-based change detection algorithms, dual ratio (DR), multi-ratio (MR) and multi-ratio fusion (MRF), are tested with full motion video data collected from an unmanned aerial vehicle (UAV) platform. The dataset suffers from several practical issues that generally hinder change detection utility and performance: including image registration error, changes in perspective and significant illumination changes. The ratio-based approaches are compared to change detection methods from literature and are found to be more robust to these practical issues. MRF is found to be the top performing method exhibiting a 10% average performance advantage over the next best performing method across all false alarm regions. MRF also outperforms the next best performing method by 22% at low false alarms rates that are critical in many applications.
机译:本文使用从无人飞行器(UAV)平台收集的全动态视频数据测试了三种基于比率的变化检测算法,即双比率(DR),多比率(MR)和多比率融合(MRF)。该数据集存在一些实际的问题,这些问题通常会阻碍更改检测的实用性和性能:包括图像配准错误,视角更改和重大照明更改。将基于比率的方法与文献中的变化检测方法进行了比较,发现它们对这些实际问题更为可靠。发现MRF是在所有误报区域中表现出优于次佳方法的10%的平均性能优势的最佳方法。在许多应用中至关重要的低误报率下,MRF的性能也比次优方法高出22%。

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