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<p>The advancement of high-quality camera technology has increased the demand for efficient video analysis methods. Current methods mostly rely on matrix-based approaches, which break data structures and lose some spatial information. This paper proposes a novel approach (TRLRTTV) that combines Low Rank Tensor Ring decomposition and Tensor Total Variation regularization for moving objects detection (MOD). For static background detection, the tensor ring (TR) decomposition is utilized to extract low rank information, and low rank assumption is placed on tensor factors instead of the original data. For moving objects, a tensor total variation model with <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_18059_Article_IEq1.gif" Format="GIF" Height="20" Rendition="HTML" Resolution="72" Type="Linedraw" Width="24" /> </InlineMediaObject> <EquationSource Format="TEX">\(l_{1/2}\)</EquationSource> </InlineEquation> regularization is employed to ensure the representation of foreground information along the spatio-temporal direction. The results demonstrate that, compared to existing methods, the proposed algorithm achieves a 3%-8% performance improvement in background separation and the comprehensive performance metric <i>f</i>. Furthermore, the proposed method ensures robustness against noise interference, such as Gaussian and salt-and-pepper noise and more suitable for higher-dimensional video processing.</p>

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Moving objects detection based on tensor ring low rank decomposition

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Ruixuan Chen,
  • Xusheng Li,
  • Chenda Chen,
  • Shiyu Zhu,
  • Qipeng Chen,
  • Jianting Cao

摘要

The advancement of high-quality camera technology has increased the demand for efficient video analysis methods. Current methods mostly rely on matrix-based approaches, which break data structures and lose some spatial information. This paper proposes a novel approach (TRLRTTV) that combines Low Rank Tensor Ring decomposition and Tensor Total Variation regularization for moving objects detection (MOD). For static background detection, the tensor ring (TR) decomposition is utilized to extract low rank information, and low rank assumption is placed on tensor factors instead of the original data. For moving objects, a tensor total variation model with \(l_{1/2}\) regularization is employed to ensure the representation of foreground information along the spatio-temporal direction. The results demonstrate that, compared to existing methods, the proposed algorithm achieves a 3%-8% performance improvement in background separation and the comprehensive performance metric f. Furthermore, the proposed method ensures robustness against noise interference, such as Gaussian and salt-and-pepper noise and more suitable for higher-dimensional video processing.