One of the most difficult scenarios for unsupervised segmentation of moving objects is found in nighttime videos where the main challenges are the poor illumination conditions resulting in low-visibility of objects, very strong lights, surface-reflected light, a great variance of light intensity, sudden illumination changes, hard shadows, camouflaged objects, and noise. This paper proposes a novel method, coined COLBMOG (COLlinearity Boosted MOG), devised specifically for the foreground segmentation in nighttime videos, that shows the ability to overcome some of the limitations of state-of-the-art methods and still perform well in daytime scenarios. It is a texture-based classification method, using local texture modeling, complemented by a color-based classification method. The local texture at the pixel neighborhood is modeled as an $N$-dimensional vector. For a given pixel, the classification is based on the collinearity between this feature in the input frame and the reference background frame. For this purpose, a multimodal temporal model of the collinearity between texture vectors of background pixels is maintained. COLBMOG was objectively evaluated using the ChangeDetection.net (CDnet) 2014, Night Videos category, benchmark. COLBMOG ranks first among all the unsupervised methods. A detailed analysis of the results revealed the superior performance of the proposed method compared to the best performing state-of-the-art methods in this category, particularly evident in the presence of the most complex situations where all the algorithms tend to fail.

在夜间录像中，无监督分割运动物体是最困难的情况之一，其主要挑战是照明条件差，导致物体的可见度低，强光，表面反射光，光强度变化大，突然的照明变化，硬阴影，伪装的物体和噪音。本文提出了一种新颖的方法，称为COLBMOG（COLlinearity Boosted MOG），该方法专为夜间视频中的前景分割而设计，该方法具有克服现有方法某些局限性的能力，并且在白天仍然表现良好场景。它是一种基于纹理的分类方法，使用局部纹理建模，并辅以基于颜色的分类方法。像素邻域的局部纹理被建模为$ñ$维向量。对于给定的像素，分类基于输入帧中此特征与参考背景帧之间的共线性。为此，维持背景像素的纹理矢量之间的共线性的多峰时间模型。使用ChangeDetection.net（CDnet）2014夜间视频类别基准对COLBMOG进行了客观评估。在所有无监督方法中，COLBMOG排名第一。对结果的详细分析显示，与同类最佳性能的最新方法相比，该方法具有更好的性能，在所有算法都趋于失败的最复杂情况下，这一点尤为明显。