IEICE TRANSACTIONS on Information
Robust Visual Tracking Using Sparse Discriminative Graph Embedding
Summary :
For robust visual tracking, the main challenges of a subspace representation model can be attributed to the difficulty in handling various appearances of the target object. Traditional subspace learning tracking algorithms neglected the discriminative correlation between different multi-view target samples and the effectiveness of sparse subspace learning. For learning a better subspace representation model, we designed a discriminative graph to model both the labeled target samples with various appearances and the updated foreground and background samples, which are selected using an incremental updating scheme. The proposed discriminative graph structure not only can explicitly capture multi-modal intraclass correlations within labeled samples but also can obtain a balance between within-class local manifold and global discriminative information from foreground and background samples. Based on the discriminative graph, we achieved a sparse embedding by using L2,1-norm, which is incorporated to select relevant features and learn transformation in a unified framework. In a tracking procedure, the subspace learning is embedded into a Bayesian inference framework using compound motion estimation and a discriminative observation model, which significantly makes localization effective and accurate. Experiments on several videos have demonstrated that the proposed algorithm is robust for dealing with various appearances, especially in dynamically changing and clutter situations, and has better performance than alternatives reported in the recent literature.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.4 pp.938-947
- Publication Date
- 2015/04/01
- Publicized
- 2015/01/19
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDP7419
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Jidong ZHAO
University of Electronic Science & Technology of China
Jingjing LI
University of Electronic Science & Technology of China
Ke LU
University of Electronic Science & Technology of China
Keyword
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Jidong ZHAO, Jingjing LI, Ke LU, "Robust Visual Tracking Using Sparse Discriminative Graph Embedding" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 4, pp. 938-947, April 2015, doi: 10.1587/transinf.2014EDP7419.
Abstract: For robust visual tracking, the main challenges of a subspace representation model can be attributed to the difficulty in handling various appearances of the target object. Traditional subspace learning tracking algorithms neglected the discriminative correlation between different multi-view target samples and the effectiveness of sparse subspace learning. For learning a better subspace representation model, we designed a discriminative graph to model both the labeled target samples with various appearances and the updated foreground and background samples, which are selected using an incremental updating scheme. The proposed discriminative graph structure not only can explicitly capture multi-modal intraclass correlations within labeled samples but also can obtain a balance between within-class local manifold and global discriminative information from foreground and background samples. Based on the discriminative graph, we achieved a sparse embedding by using L2,1-norm, which is incorporated to select relevant features and learn transformation in a unified framework. In a tracking procedure, the subspace learning is embedded into a Bayesian inference framework using compound motion estimation and a discriminative observation model, which significantly makes localization effective and accurate. Experiments on several videos have demonstrated that the proposed algorithm is robust for dealing with various appearances, especially in dynamically changing and clutter situations, and has better performance than alternatives reported in the recent literature.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7419/_p
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@ARTICLE{e98-d_4_938,
author={Jidong ZHAO, Jingjing LI, Ke LU, },
journal={IEICE TRANSACTIONS on Information},
title={Robust Visual Tracking Using Sparse Discriminative Graph Embedding},
year={2015},
volume={E98-D},
number={4},
pages={938-947},
abstract={For robust visual tracking, the main challenges of a subspace representation model can be attributed to the difficulty in handling various appearances of the target object. Traditional subspace learning tracking algorithms neglected the discriminative correlation between different multi-view target samples and the effectiveness of sparse subspace learning. For learning a better subspace representation model, we designed a discriminative graph to model both the labeled target samples with various appearances and the updated foreground and background samples, which are selected using an incremental updating scheme. The proposed discriminative graph structure not only can explicitly capture multi-modal intraclass correlations within labeled samples but also can obtain a balance between within-class local manifold and global discriminative information from foreground and background samples. Based on the discriminative graph, we achieved a sparse embedding by using L2,1-norm, which is incorporated to select relevant features and learn transformation in a unified framework. In a tracking procedure, the subspace learning is embedded into a Bayesian inference framework using compound motion estimation and a discriminative observation model, which significantly makes localization effective and accurate. Experiments on several videos have demonstrated that the proposed algorithm is robust for dealing with various appearances, especially in dynamically changing and clutter situations, and has better performance than alternatives reported in the recent literature.},
keywords={},
doi={10.1587/transinf.2014EDP7419},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Robust Visual Tracking Using Sparse Discriminative Graph Embedding
T2 - IEICE TRANSACTIONS on Information
SP - 938
EP - 947
AU - Jidong ZHAO
AU - Jingjing LI
AU - Ke LU
PY - 2015
DO - 10.1587/transinf.2014EDP7419
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2015
AB - For robust visual tracking, the main challenges of a subspace representation model can be attributed to the difficulty in handling various appearances of the target object. Traditional subspace learning tracking algorithms neglected the discriminative correlation between different multi-view target samples and the effectiveness of sparse subspace learning. For learning a better subspace representation model, we designed a discriminative graph to model both the labeled target samples with various appearances and the updated foreground and background samples, which are selected using an incremental updating scheme. The proposed discriminative graph structure not only can explicitly capture multi-modal intraclass correlations within labeled samples but also can obtain a balance between within-class local manifold and global discriminative information from foreground and background samples. Based on the discriminative graph, we achieved a sparse embedding by using L2,1-norm, which is incorporated to select relevant features and learn transformation in a unified framework. In a tracking procedure, the subspace learning is embedded into a Bayesian inference framework using compound motion estimation and a discriminative observation model, which significantly makes localization effective and accurate. Experiments on several videos have demonstrated that the proposed algorithm is robust for dealing with various appearances, especially in dynamically changing and clutter situations, and has better performance than alternatives reported in the recent literature.
ER -
English
