Combining Color Features for Real-Time Correlation Tracking
Summary :
Correlation filter-based approaches achieve competitive results in visual tracking, but the traditional correlation tracking methods failed in mining the color information of the videos. To address this issue, we propose a novel tracker combined with color features in a correlation filter framework, which extracts not only gray but also color information as the feature maps to compute the maximum response location via multi-channel correlation filters. In particular, we modify the label function of the conventional classifier to improve positioning accuracy and employ a discriminative correlation filter to handle scale variations. Experiments are performed on 35 challenging benchmark color sequences. And the results clearly show that our method outperforms state-of-the-art tracking approaches while operating in real-time.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.1 pp.225-228
- Publication Date
- 2017/01/01
- Publicized
- 2016/10/04
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016EDL8053
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Yulong XU
PLA University of Science and Technology (PLAUST)
Zhuang MIAO
PLA University of Science and Technology (PLAUST)
Jiabao WANG
PLA University of Science and Technology (PLAUST)
Yang LI
PLA University of Science and Technology (PLAUST)
Hang LI
PLA University of Science and Technology (PLAUST)
Yafei ZHANG
PLA University of Science and Technology (PLAUST)
Weiguang XU
PLA University of Science and Technology (PLAUST)
Zhisong PAN
PLA University of Science and Technology (PLAUST)
Keyword
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Yulong XU, Zhuang MIAO, Jiabao WANG, Yang LI, Hang LI, Yafei ZHANG, Weiguang XU, Zhisong PAN, "Combining Color Features for Real-Time Correlation Tracking" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 1, pp. 225-228, January 2017, doi: 10.1587/transinf.2016EDL8053.
Abstract: Correlation filter-based approaches achieve competitive results in visual tracking, but the traditional correlation tracking methods failed in mining the color information of the videos. To address this issue, we propose a novel tracker combined with color features in a correlation filter framework, which extracts not only gray but also color information as the feature maps to compute the maximum response location via multi-channel correlation filters. In particular, we modify the label function of the conventional classifier to improve positioning accuracy and employ a discriminative correlation filter to handle scale variations. Experiments are performed on 35 challenging benchmark color sequences. And the results clearly show that our method outperforms state-of-the-art tracking approaches while operating in real-time.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDL8053/_p
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@ARTICLE{e100-d_1_225,
author={Yulong XU, Zhuang MIAO, Jiabao WANG, Yang LI, Hang LI, Yafei ZHANG, Weiguang XU, Zhisong PAN, },
journal={IEICE TRANSACTIONS on Information},
title={Combining Color Features for Real-Time Correlation Tracking},
year={2017},
volume={E100-D},
number={1},
pages={225-228},
abstract={Correlation filter-based approaches achieve competitive results in visual tracking, but the traditional correlation tracking methods failed in mining the color information of the videos. To address this issue, we propose a novel tracker combined with color features in a correlation filter framework, which extracts not only gray but also color information as the feature maps to compute the maximum response location via multi-channel correlation filters. In particular, we modify the label function of the conventional classifier to improve positioning accuracy and employ a discriminative correlation filter to handle scale variations. Experiments are performed on 35 challenging benchmark color sequences. And the results clearly show that our method outperforms state-of-the-art tracking approaches while operating in real-time.},
keywords={},
doi={10.1587/transinf.2016EDL8053},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Combining Color Features for Real-Time Correlation Tracking
T2 - IEICE TRANSACTIONS on Information
SP - 225
EP - 228
AU - Yulong XU
AU - Zhuang MIAO
AU - Jiabao WANG
AU - Yang LI
AU - Hang LI
AU - Yafei ZHANG
AU - Weiguang XU
AU - Zhisong PAN
PY - 2017
DO - 10.1587/transinf.2016EDL8053
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2017
AB - Correlation filter-based approaches achieve competitive results in visual tracking, but the traditional correlation tracking methods failed in mining the color information of the videos. To address this issue, we propose a novel tracker combined with color features in a correlation filter framework, which extracts not only gray but also color information as the feature maps to compute the maximum response location via multi-channel correlation filters. In particular, we modify the label function of the conventional classifier to improve positioning accuracy and employ a discriminative correlation filter to handle scale variations. Experiments are performed on 35 challenging benchmark color sequences. And the results clearly show that our method outperforms state-of-the-art tracking approaches while operating in real-time.
ER -
English
