IEICE TRANSACTIONS on Information
Robust and Adaptive Object Tracking via Correspondence Clustering
Summary :
We propose a new visual tracking method, where the target appearance is represented by combining color distribution and keypoints. Firstly, the object is localized via a keypoint-based tracking and matching strategy, where a new clustering method is presented to remove outliers. Secondly, the tracking confidence is evaluated by the color template. According to the tracking confidence, the local and global keypoints matching can be performed adaptively. Finally, we propose a target appearance update method in which the new appearance can be learned and added to the target model. The proposed tracker is compared with five state-of-the-art tracking methods on a recent benchmark dataset. Both qualitative and quantitative evaluations show that our method has favorable performance.
- Publication
- IEICE TRANSACTIONS on Information Vol.E99-D No.10 pp.2664-2667
- Publication Date
- 2016/10/01
- Publicized
- 2016/06/23
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016EDL8065
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Bo WU
University of Electronic Science and Technology of China,Henan Normal University
Yurui XIE
University of Electronic Science and Technology of China
Wang LUO
Nari Group Corporation (State Grid Electric Power Research Institute)
Keyword
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Bo WU, Yurui XIE, Wang LUO, "Robust and Adaptive Object Tracking via Correspondence Clustering" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 10, pp. 2664-2667, October 2016, doi: 10.1587/transinf.2016EDL8065.
Abstract: We propose a new visual tracking method, where the target appearance is represented by combining color distribution and keypoints. Firstly, the object is localized via a keypoint-based tracking and matching strategy, where a new clustering method is presented to remove outliers. Secondly, the tracking confidence is evaluated by the color template. According to the tracking confidence, the local and global keypoints matching can be performed adaptively. Finally, we propose a target appearance update method in which the new appearance can be learned and added to the target model. The proposed tracker is compared with five state-of-the-art tracking methods on a recent benchmark dataset. Both qualitative and quantitative evaluations show that our method has favorable performance.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDL8065/_p
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@ARTICLE{e99-d_10_2664,
author={Bo WU, Yurui XIE, Wang LUO, },
journal={IEICE TRANSACTIONS on Information},
title={Robust and Adaptive Object Tracking via Correspondence Clustering},
year={2016},
volume={E99-D},
number={10},
pages={2664-2667},
abstract={We propose a new visual tracking method, where the target appearance is represented by combining color distribution and keypoints. Firstly, the object is localized via a keypoint-based tracking and matching strategy, where a new clustering method is presented to remove outliers. Secondly, the tracking confidence is evaluated by the color template. According to the tracking confidence, the local and global keypoints matching can be performed adaptively. Finally, we propose a target appearance update method in which the new appearance can be learned and added to the target model. The proposed tracker is compared with five state-of-the-art tracking methods on a recent benchmark dataset. Both qualitative and quantitative evaluations show that our method has favorable performance.},
keywords={},
doi={10.1587/transinf.2016EDL8065},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Robust and Adaptive Object Tracking via Correspondence Clustering
T2 - IEICE TRANSACTIONS on Information
SP - 2664
EP - 2667
AU - Bo WU
AU - Yurui XIE
AU - Wang LUO
PY - 2016
DO - 10.1587/transinf.2016EDL8065
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2016
AB - We propose a new visual tracking method, where the target appearance is represented by combining color distribution and keypoints. Firstly, the object is localized via a keypoint-based tracking and matching strategy, where a new clustering method is presented to remove outliers. Secondly, the tracking confidence is evaluated by the color template. According to the tracking confidence, the local and global keypoints matching can be performed adaptively. Finally, we propose a target appearance update method in which the new appearance can be learned and added to the target model. The proposed tracker is compared with five state-of-the-art tracking methods on a recent benchmark dataset. Both qualitative and quantitative evaluations show that our method has favorable performance.
ER -
English
