光照突然变化下的背景减法

L. Vosters, Caifeng Shan, T. Gritti
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引用次数: 41

摘要

光照突然变化下的鲁棒背景减法是一个具有挑战性的问题。在本文中,我们提出了一种将特征背景算法与统计照明模型相结合的方法来解决这个问题。第一种算法用于对输入帧进行粗略重建,第二种算法用于改善前景分割。我们通过检测可靠的背景和前景像素引入了一种在线空间似然模型。实验结果表明,与几种最先进的算法相比,我们的方法始终具有更高的精度
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Background Subtraction under Sudden Illumination Changes
Robust background subtraction under sudden illuminationchanges is a challenging problem. In this paper, wepropose an approach to address this issue, which combinesthe Eigenbackground algorithm together with a statisticalillumination model. The rst algorithm is used to give arough reconstruction of the input frame, while the secondone improves the foreground segmentation. We introduce anonline spatial likelihood model by detecting reliable backgroundand foreground pixels. Experimental results illustratethat our approach achieves consistently higher accuracycompared to several state-of-the-art algorithms
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