Background subtraction is a fundamental video analysis technique that consists of creation of a background model that allows distinguishing foreground pixels. We present a new method in which the image sequence is assumed to be made up of the sum of a low-rank background matrix and a dynamic tree-structured sparse matrix. The decomposition task is then solved using our approximated Robust Principal Component Analysis (ARPCA) method which is an extension to the RPCA that can handle camera motion and noise. Our model dynamically estimates the support of the foreground regions via a superpixel generation step, so that spatial coherence can be imposed on these regions. Unlike conventional smoothness constraints such as MRF, our method is able to obtain crisp and meaningful foreground regions, and in general, handles large dynamic background motion better. To reduce the dimensionality and the curse of scale that is persistent in the RPCA-based methods, we model the background via Column Subset Selection Problem, that reduces the order of complexity and hence decreases computation time. Comprehensive evaluation on four benchmark datasets demonstrate the effectiveness of our method in outperforming state-of-the-art alternatives.

中文翻译：

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树状稀疏RPCA的前景分割

背景减法是一种基本的视频分析技术，包括创建可区分前景像素的背景模型。我们提出了一种新方法，其中假定图像序列由低秩背景矩阵和动态树结构稀疏矩阵之和组成。然后，使用我们的近似稳健主成分分析（ARPCA）方法解决分解任务，该方法是RPCA的扩展，可以处理摄像机的运动和噪声。我们的模型通过超像素生成步骤动态估算前景区域的支持度，以便可以将空间相干性强加于这些区域。与传统的平滑度约束（例如MRF）不同，我们的方法能够获得清晰且有意义的前景区域，通常，更好地处理大动态背景运动。为了减少基于RPCA的方法的维数和规模诅咒，我们通过“列子集选择问题”对背景进行建模，这降低了复杂性的顺序，从而减少了计算时间。对四个基准数据集的综合评估证明了我们的方法在优于最新替代方法方面的有效性。