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Proceedings Paper

Moving object detection based on 3D total variation and weighted nonconvex nuclear norm
Author(s): Wenzheng Zhao; Qingqing Cui; Youguang Chen
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Paper 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.

Paper Details

Date Published: 9 August 2018
PDF: 7 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061H (9 August 2018); doi: 10.1117/12.2503275
Show Author Affiliations
Wenzheng Zhao, East China Normal Univ. (China)
Qingqing Cui, East China Normal Univ. (China)
Youguang Chen, East China Normal Univ. (China)

Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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