IEICE TRANSACTIONS on Information
Inequality-Constrained RPCA for Shadow Removal and Foreground Detection
Summary :
State-of-the-art background subtraction and foreground detection methods still face a variety of challenges, including illumination changes, camouflage, dynamic backgrounds, shadows, intermittent object motion. Detection of foreground elements via the robust principal component analysis (RPCA) method and its extensions based on low-rank and sparse structures have been conducted to achieve good performance in many scenes of the datasets, such as Changedetection.net (CDnet); however, the conventional RPCA method does not handle shadows well. To address this issue, we propose an approach that considers observed video data as the sum of three parts, namely a row-rank background, sparse moving objects and moving shadows. Next, we cast inequality constraints on the basic RPCA model and use an alternating direction method of multipliers framework combined with Rockafeller multipliers to derive a closed-form solution of the shadow matrix sub-problem. Our experiments have demonstrated that our method works effectively on challenging datasets that contain shadows.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.6 pp.1256-1259
- Publication Date
- 2015/06/01
- Publicized
- 2015/03/02
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDL8234
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Hang LI
PLA University of Science and Technology (PLAUST)
Yafei ZHANG
PLA University of Science and Technology (PLAUST)
Jiabao WANG
PLA University of Science and Technology (PLAUST)
Yulong XU
PLA University of Science and Technology (PLAUST)
Yang LI
PLA University of Science and Technology (PLAUST)
Zhisong PAN
PLA University of Science and Technology (PLAUST)
Keyword
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Hang LI, Yafei ZHANG, Jiabao WANG, Yulong XU, Yang LI, Zhisong PAN, "Inequality-Constrained RPCA for Shadow Removal and Foreground Detection" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 6, pp. 1256-1259, June 2015, doi: 10.1587/transinf.2014EDL8234.
Abstract: State-of-the-art background subtraction and foreground detection methods still face a variety of challenges, including illumination changes, camouflage, dynamic backgrounds, shadows, intermittent object motion. Detection of foreground elements via the robust principal component analysis (RPCA) method and its extensions based on low-rank and sparse structures have been conducted to achieve good performance in many scenes of the datasets, such as Changedetection.net (CDnet); however, the conventional RPCA method does not handle shadows well. To address this issue, we propose an approach that considers observed video data as the sum of three parts, namely a row-rank background, sparse moving objects and moving shadows. Next, we cast inequality constraints on the basic RPCA model and use an alternating direction method of multipliers framework combined with Rockafeller multipliers to derive a closed-form solution of the shadow matrix sub-problem. Our experiments have demonstrated that our method works effectively on challenging datasets that contain shadows.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDL8234/_p
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@ARTICLE{e98-d_6_1256,
author={Hang LI, Yafei ZHANG, Jiabao WANG, Yulong XU, Yang LI, Zhisong PAN, },
journal={IEICE TRANSACTIONS on Information},
title={Inequality-Constrained RPCA for Shadow Removal and Foreground Detection},
year={2015},
volume={E98-D},
number={6},
pages={1256-1259},
abstract={State-of-the-art background subtraction and foreground detection methods still face a variety of challenges, including illumination changes, camouflage, dynamic backgrounds, shadows, intermittent object motion. Detection of foreground elements via the robust principal component analysis (RPCA) method and its extensions based on low-rank and sparse structures have been conducted to achieve good performance in many scenes of the datasets, such as Changedetection.net (CDnet); however, the conventional RPCA method does not handle shadows well. To address this issue, we propose an approach that considers observed video data as the sum of three parts, namely a row-rank background, sparse moving objects and moving shadows. Next, we cast inequality constraints on the basic RPCA model and use an alternating direction method of multipliers framework combined with Rockafeller multipliers to derive a closed-form solution of the shadow matrix sub-problem. Our experiments have demonstrated that our method works effectively on challenging datasets that contain shadows.},
keywords={},
doi={10.1587/transinf.2014EDL8234},
ISSN={1745-1361},
month={June},}
Copy
TY - JOUR
TI - Inequality-Constrained RPCA for Shadow Removal and Foreground Detection
T2 - IEICE TRANSACTIONS on Information
SP - 1256
EP - 1259
AU - Hang LI
AU - Yafei ZHANG
AU - Jiabao WANG
AU - Yulong XU
AU - Yang LI
AU - Zhisong PAN
PY - 2015
DO - 10.1587/transinf.2014EDL8234
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2015
AB - State-of-the-art background subtraction and foreground detection methods still face a variety of challenges, including illumination changes, camouflage, dynamic backgrounds, shadows, intermittent object motion. Detection of foreground elements via the robust principal component analysis (RPCA) method and its extensions based on low-rank and sparse structures have been conducted to achieve good performance in many scenes of the datasets, such as Changedetection.net (CDnet); however, the conventional RPCA method does not handle shadows well. To address this issue, we propose an approach that considers observed video data as the sum of three parts, namely a row-rank background, sparse moving objects and moving shadows. Next, we cast inequality constraints on the basic RPCA model and use an alternating direction method of multipliers framework combined with Rockafeller multipliers to derive a closed-form solution of the shadow matrix sub-problem. Our experiments have demonstrated that our method works effectively on challenging datasets that contain shadows.
ER -
English
