Quaternion-Based Robust PCA for Efficient Moving Target Detection and Background Recovery in Color Videos Article Swipe

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Liyang Wang , Shiqian Wu , Shun Fang , Qile Zhu , Jiaxin Wu , Sos Again ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2507.19730

Moving target detection is a challenging computer vision task aimed at generating accurate segmentation maps in diverse in-the-wild color videos captured by static cameras. If backgrounds and targets can be simultaneously extracted and recombined, such synthetic data can significantly enrich annotated in-the-wild datasets and enhance the generalization ability of deep models. Quaternion-based RPCA (QRPCA) is a promising unsupervised paradigm for color image processing. However, in color video processing, Quaternion Singular Value Decomposition (QSVD) incurs high computational costs, and rank-1 quaternion matrix fails to yield rank-1 color channels. In this paper, we reduce the computational complexity of QSVD to o(1) by utilizing a quaternion Riemannian manifold. Furthermor, we propose the universal QRPCA (uQRPCA) framework, which achieves a balance in simultaneously segmenting targets and recovering backgrounds from color videos. Moreover, we expand to uQRPCA+ by introducing the Color Rank-1 Batch (CR1B) method to further process and obtain the ideal low-rank background across color channels. Experiments demonstrate our uQRPCA+ achieves State Of The Art (SOTA) performance on moving target detection and background recovery tasks compared to existing open-source methods. Our implementation is publicly available on GitHub at https://github.com/Ruchtech/uQRPCA

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2507.19730
PDF: https://arxiv.org/pdf/2507.19730
OA Status: green
OpenAlex ID: https://openalex.org/W4417267272

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DOI: https://doi.org/10.48550/arxiv.2507.19730

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Title: Quaternion-Based Robust PCA for Efficient Moving Target Detection and Background Recovery in Color Videos

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-07-26

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Authors: Liyang Wang, Shiqian Wu, Shun Fang, Qile Zhu, Jiaxin Wu, Sos Again

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Landing page: https://arxiv.org/abs/2507.19730

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PDF URL: https://arxiv.org/pdf/2507.19730

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