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首页> 外文期刊>Journal of electronic imaging >Video denoising and moving object detection by rank-1 and total variation regularization on robust principal component analysis framework
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Video denoising and moving object detection by rank-1 and total variation regularization on robust principal component analysis framework

机译：通过Rank-1和鲁棒主成分分析框架上的Rank-1和总变化正规化的视频去噪和移动物体检测

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摘要

With the complexity of the video environment and the problem of possible noise during data transmission, traditional robust principal component analysis (RPCA) failed to obtain the lowest rank representation from corrupted data. A method of video denoising and an object detection algorithm based on the RPCA model with total variation and rank-1 constraint (TVR1-RPCA) is proposed; it employs the more refined prior representations for the static and dynamic components of the video sequences. The proposed method is based on RPCA under the framework of low-rank sparse decomposition; the rank-1 constraint is exploited to describe the strong low-rank property of the background layer, TV regularization is combined with l1 regularization to constrain the sparsity and spatial continuity of the foreground component, and l2 norm regularization is combined to constrain the noise to make up for the deficiencies of the existing RPCA model. In addition, an efficient algorithm based on the alternating direction method of multipliers is designed to solve the proposed video denoising and moving object detection issues. Our experiments on static and moving camera videos demonstrate that the proposed method is superior to the state-of-the-art methods in terms of denoising capability and detection accuracy.
机译：随着视频环境的复杂性和数据传输期间可能的噪声问题，传统的鲁棒主成分分析（RPCA）无法获得损坏的数据中的最低排名表示。提出了一种基于RPCA模型的视频去噪和具有总变化和秩1约束（TVR1-RPCA）的对象检测算法;它采用了视频序列的静态和动态组件的更精细的先前表示。所提出的方法基于RPCA在低级别稀疏分解的框架下;利用等级-1约束来描述背景层的强低级特性，电视正则化与L1正则化相结合，以限制前景分量的稀疏性和空间连续性，并且将L2规范正规组合以限制噪声来限制噪声弥补现有RPCA模型的缺陷。另外，基于乘法器的交替方向方法的高效算法旨在解决所提出的视频去噪和移动物体检测问题。我们对静态和移动相机视频的实验表明，在去噪能力和检测准确性方面，所提出的方法优于最先进的方法。

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