论文标题
背景/前景分离的平滑稳健张量完成，缺失像素：带有收敛保证的新算法
Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee
论文作者
论文摘要
这项研究的目的是通过将视频采集，视频恢复，背景/前景分离与单个框架相结合，以解决背景/前景分离和缺少像素的问题。为此，提出了平滑的稳健张量完成（SRTC）模型，以分别恢复数据并将其分解到静态背景和光滑的前景中。具体而言，静态背景是由低级塔克分解模型建模的，光滑的前景（移动对象）由时空连续性建模，该时空连续性由总变异正则化强制。实施了基于张量近端交替最小化（TENPAM）的有效算法，以在非常温和的条件下使用全球收敛保证解决提出的模型。对真实数据的广泛实验表明，所提出的方法显着优于使用缺失像素的背景/前景分离的最新方法。
The objective of this study is to address the problem of background/foreground separation with missing pixels by combining the video acquisition, video recovery, background/foreground separation into a single framework. To achieve this, a smooth robust tensor completion (SRTC) model is proposed to recover the data and decompose it into the static background and smooth foreground, respectively. Specifically, the static background is modeled by the low-rank tucker decomposition and the smooth foreground (moving objects) is modeled by the spatiotemporal continuity, which is enforced by the total variation regularization. An efficient algorithm based on tensor proximal alternating minimization (tenPAM) is implemented to solve the proposed model with global convergence guarantee under very mild conditions. Extensive experiments on real data demonstrate that the proposed method significantly outperforms the state-of-the-art approaches for background/foreground separation with missing pixels.
