In order to effectively detect dim-small targets in complex scenes, background suppression is applied to highlight the targets. This paper presents a statistical clustering partitioning low-rank background modeling algorithm (SCPLBMA), which clusters the image into several patches based on image statistics. The image matrix of each patch is decomposed into low-rank matrix and sparse matrix in the SCPLBMA. The background of the original video frames is reconstructed from the low-rank matrices, and the targets can be obtained by subtracting the background. Experiments on different scenes show that the SCPLBMA can effectively suppress the background and textures and equalize the residual noise with gray levels significantly lower than that of the targets. Thus, the difference images obtain good stationary characteristics, and the contrast between the targets and the residual backgrounds is significantly improved. Compared with six other algorithms, the SCPLBMA significantly improved the target detection rates of single-frame threshold segmentation.

中文翻译：

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基于统计聚类划分的背景建模

为了有效地检测复杂场景中的昏暗目标，应用背景抑制来突出显示目标。本文提出了一种统计聚类划分低秩背景建模算法（SCPLBMA），该算法基于图像统计信息将图像聚类为几个小块。每个色块的图像矩阵在SCPLBMA中分解为低秩矩阵和稀疏矩阵。从低秩矩阵重构原始视频帧的背景，并且可以通过减去背景来获得目标。在不同场景上进行的实验表明，SCPLBMA可以有效地抑制背景和纹理，并以明显低于目标的灰度来均衡残留噪声。因此，差异图像获得了良好的静止特性，目标和残留背景之间的对比度大大提高。与其他六种算法相比，SCPLBMA显着提高了单帧阈值分割的目标检测率。