Moving object detection based on 3D total variation and weighted nonconvex nuclear norm

Wenzheng Zhao; Qingqing Cui; Youguang Chen

doi:10.1117/12.2503275

9 August 2018 Moving object detection based on 3D total variation and weighted nonconvex nuclear norm

Wenzheng Zhao, Qingqing Cui, Youguang Chen

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108061H (2018) https://doi.org/10.1117/12.2503275
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

Moving object detection and background estimation are important steps in numerous computer vision applications. Low rank and sparse representation based methods have attracted wide attention in background modeling field. However, many existing methods ignore the spatio-temporal information of the foreground. In this paper, a new low-rank and sparse representation model for moving object detection is proposed, in which we regard the image sequence as being made up of the sum of a low-rank static background matrix, a sparse foreground matrix and a sparser dynamic background matrix. The 3D total variation regularizer and weighted nonconvex nuclear norm are incorporated to refine our model. Extensive experiments on challenging datasets demonstrate that our method works effectively and outperforms many state-of-the-art approaches.

Citation Download Citation

Wenzheng Zhao, Qingqing Cui, and Youguang Chen "Moving object detection based on 3D total variation and weighted nonconvex nuclear norm", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061H (9 August 2018); https://doi.org/10.1117/12.2503275

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KEYWORDS

3D modeling

Video

Data modeling

Video surveillance

Visual process modeling

Computer vision technology

Machine vision

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