IEEE Trans Image Process. 2015 Aug;24(8):2502-14. doi: 10.1109/TIP.2015.2419084. Epub 2015 Apr 1.
Low rank and sparse representation based methods, which make few specific assumptions about the background, have recently attracted wide attention in background modeling. With these methods, moving objects in the scene are modeled as pixel-wised sparse outliers. However, in many practical scenarios, the distributions of these moving parts are not truly pixel-wised sparse but structurally sparse. Meanwhile a robust analysis mechanism is required to handle background regions or foreground movements with varying scales. Based on these two observations, we first introduce a class of structured sparsity-inducing norms to model moving objects in videos. In our approach, we regard the observed sequence as being constituted of two terms, a low-rank matrix (background) and a structured sparse outlier matrix (foreground). Next, in virtue of adaptive parameters for dynamic videos, we propose a saliency measurement to dynamically estimate the support of the foreground. Experiments on challenging well known data sets demonstrate that the proposed approach outperforms the state-of-the-art methods and works effectively on a wide range of complex videos.
基于低秩和稀疏表示的方法，对背景的假设很少，最近在背景建模中引起了广泛关注。这些方法将场景中的运动物体建模为逐像素稀疏的异常值。然而，在许多实际场景中，这些运动部分的分布并不是真正的逐像素稀疏，而是结构稀疏。同时，需要一种稳健的分析机制来处理具有不同尺度的背景区域或前景运动。基于这两个观察结果，我们首先引入了一类结构稀疏诱导范数来对视频中的运动物体进行建模。在我们的方法中，我们将观察到的序列视为由两个项组成，一个低秩矩阵（背景）和一个结构稀疏的异常值矩阵（前景）。接下来，利用适用于动态视频的自适应参数，我们提出了一种显著度测量方法来动态估计前景的支撑。在具有挑战性的知名数据集上的实验表明，所提出的方法优于最先进的方法，并能有效地处理广泛的复杂视频。
