IEICE TRANSACTIONS on Information
Kernel-Based On-Line Object Tracking Combining both Local Description and Global Representation
Summary :
This paper proposes a novel method for object tracking by combining local feature and global template-based methods. The proposed algorithm consists of two stages from coarse to fine. The first stage applies on-line classifiers to match the corresponding keypoints between the input frame and the reference frame. Thus a rough motion parameter can be estimated using RANSAC. The second stage employs kernel-based global representation in successive frames to refine the motion parameter. In addition, we use the kernel weight obtained during the second stage to guide the on-line learning process of the keypoints' description. Experimental results demonstrate the effectiveness of the proposed technique.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.1 pp.159-162
- Publication Date
- 2013/01/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.159
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Quan MIAO, Guijin WANG, Xinggang LIN, "Kernel-Based On-Line Object Tracking Combining both Local Description and Global Representation" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 1, pp. 159-162, January 2013, doi: 10.1587/transinf.E96.D.159.
Abstract: This paper proposes a novel method for object tracking by combining local feature and global template-based methods. The proposed algorithm consists of two stages from coarse to fine. The first stage applies on-line classifiers to match the corresponding keypoints between the input frame and the reference frame. Thus a rough motion parameter can be estimated using RANSAC. The second stage employs kernel-based global representation in successive frames to refine the motion parameter. In addition, we use the kernel weight obtained during the second stage to guide the on-line learning process of the keypoints' description. Experimental results demonstrate the effectiveness of the proposed technique.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.159/_p
Copy
@ARTICLE{e96-d_1_159,
author={Quan MIAO, Guijin WANG, Xinggang LIN, },
journal={IEICE TRANSACTIONS on Information},
title={Kernel-Based On-Line Object Tracking Combining both Local Description and Global Representation},
year={2013},
volume={E96-D},
number={1},
pages={159-162},
abstract={This paper proposes a novel method for object tracking by combining local feature and global template-based methods. The proposed algorithm consists of two stages from coarse to fine. The first stage applies on-line classifiers to match the corresponding keypoints between the input frame and the reference frame. Thus a rough motion parameter can be estimated using RANSAC. The second stage employs kernel-based global representation in successive frames to refine the motion parameter. In addition, we use the kernel weight obtained during the second stage to guide the on-line learning process of the keypoints' description. Experimental results demonstrate the effectiveness of the proposed technique.},
keywords={},
doi={10.1587/transinf.E96.D.159},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Kernel-Based On-Line Object Tracking Combining both Local Description and Global Representation
T2 - IEICE TRANSACTIONS on Information
SP - 159
EP - 162
AU - Quan MIAO
AU - Guijin WANG
AU - Xinggang LIN
PY - 2013
DO - 10.1587/transinf.E96.D.159
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2013
AB - This paper proposes a novel method for object tracking by combining local feature and global template-based methods. The proposed algorithm consists of two stages from coarse to fine. The first stage applies on-line classifiers to match the corresponding keypoints between the input frame and the reference frame. Thus a rough motion parameter can be estimated using RANSAC. The second stage employs kernel-based global representation in successive frames to refine the motion parameter. In addition, we use the kernel weight obtained during the second stage to guide the on-line learning process of the keypoints' description. Experimental results demonstrate the effectiveness of the proposed technique.
ER -
English
