论文标题
L+S分解的深度无形时空RPCA网络
A Deep-Unfolded Spatiotemporal RPCA Network For L+S Decomposition
论文作者
论文摘要
基于低级别和稀疏分解的方法在许多涉及背景建模的应用中找到了它们的用途，例如混乱抑制和对象跟踪。尽管强大的主成分分析（RPCA）在执行此任务方面取得了巨大的成功，但在存在不同现象（例如闭塞，抖动和快速运动）的情况下，它可能需要数百个迭代才能收敛，并且其性能降低。另一方面，最近提出的深层展开的网络在其迭代等效物以及其他神经网络体系结构上表现出更好的准确性和改善的收敛性。在这项工作中，我们提出了一个新型的深层展开的时空RPCA（Dust-RPCA）网络，该网络明确利用了低级别组件中的时空连续性。我们对移动MNIST数据集的实验结果表明，与现有的最深层展开的RPCA网络相比，DUST-RPCA具有更好的准确性。
Low-rank and sparse decomposition based methods find their use in many applications involving background modeling such as clutter suppression and object tracking. While Robust Principal Component Analysis (RPCA) has achieved great success in performing this task, it can take hundreds of iterations to converge and its performance decreases in the presence of different phenomena such as occlusion, jitter and fast motion. The recently proposed deep unfolded networks, on the other hand, have demonstrated better accuracy and improved convergence over both their iterative equivalents as well as over other neural network architectures. In this work, we propose a novel deep unfolded spatiotemporal RPCA (DUST-RPCA) network, which explicitly takes advantage of the spatial and temporal continuity in the low-rank component. Our experimental results on the moving MNIST dataset indicate that DUST-RPCA gives better accuracy when compared with the existing state of the art deep unfolded RPCA networks.
