Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee
Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee
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DOI：
10.48550/arxiv.2203.16328
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Bo Shen;Weijun Xie;Zhen Kong
中科院分区：
文献类型：
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作者：
Bo Shen;Weijun Xie;Zhen Kong
本研究的目的是通过将视频采集、视频恢复、背景/前景分离结合到一个框架中来解决缺少像素的背景/前景分离问题。为此，提出了一种平滑鲁棒张量补全（SRTC）模型来恢复数据并将其分别分解为静态背景和平滑前景。其中，静态背景通过低秩tucker分解建模，平滑前景（运动目标）通过时空连续性建模，并通过全变分正则化实现。提出了一种基于张量近端交替最小化（tenPAM）的高效算法，在非常温和的条件下保证了模型的全局收敛性。在真实数据上进行的大量实验表明，该方法在缺少像素的背景/前景分离方面明显优于目前最先进的方法。
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.