自适应速率稀疏信号重构及其在压缩背景减除中的应用
Adaptive-Rate Sparse Signal Reconstruction With Application in Compressive Background Subtraction
摘要：
我们提出并分析了一种在线算法，用于从有限数量的线性测量重构信号序列。假设信号稀疏，未知的支持，并根据一个通用的非线性动力学模型随时间演变。我们的算法，基于最近的理论结果$\ell_1$ - $\ell_1$最小化，是递归的，并计算在每个时间的测量的数量上的飞行。作为一个例子，我们将该算法应用于压缩视频背景减法，一个问题，可以说如下：给定一组测量的图像序列与静态背景，同时重建每个图像，同时分离其前景从背景。我们的方法的性能示出序列的真实图像：我们观察到，它允许一个显着减少的数量的测量相对于国家的最先进的压缩背景减法方案。
英文摘要：
We propose and analyze an online algorithm for reconstructing a sequence of signals from a limited number of linear measurements. The signals are assumed sparse, with unknown support, and evolve over time according to a generic nonlinear dynamical model. Our algorithm, based on recent theoretical results for $\ell_1$-$\ell_1$ minimization, is recursive and computes the number of measurements to be taken at each time on-the-fly. As an example, we apply the algorithm to compressive video background subtraction, a problem that can be stated as follows: given a set of measurements of a sequence of images with a static background, simultaneously reconstruct each image while separating its foreground from the background. The performance of our method is illustrated on sequences of real images: we observe that it allows a dramatic reduction in the number of measurements with respect to state-of-the-art compressive background subtraction schemes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
