In general, effective integrating the advantages of different trackers can achieve unified performance promotion. In this work, we study the integration of multiple correlation filter (CF) trackers; propose a novel but simple tracking integration method that combines different trackers in filter level. Due to the variety of their correlation filter and features, there is no comparability between different CF tracking results for tracking integration. To tackle this, we propose twofold CF to unify these various response maps so that the results of different tracking algorithms can be compared, so as to boost the tracking performance like ensemble learning. Experiment of two CF methods integration on the data sets OTB demonstrates that the proposed method is effective and promising.
Wei WANG
Chinese Academy of Sciences,University of Chinese Academy of Sciences
Weiguang LI
Chinese Academy of Sciences,University of Chinese Academy of Sciences
Zhaoming CHEN
Chinese Academy of Sciences
Mingquan SHI
Chinese Academy of Sciences
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Wei WANG, Weiguang LI, Zhaoming CHEN, Mingquan SHI, "Twofold Correlation Filtering for Tracking Integration" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 10, pp. 2547-2550, October 2018, doi: 10.1587/transinf.2018EDL8100.
Abstract: In general, effective integrating the advantages of different trackers can achieve unified performance promotion. In this work, we study the integration of multiple correlation filter (CF) trackers; propose a novel but simple tracking integration method that combines different trackers in filter level. Due to the variety of their correlation filter and features, there is no comparability between different CF tracking results for tracking integration. To tackle this, we propose twofold CF to unify these various response maps so that the results of different tracking algorithms can be compared, so as to boost the tracking performance like ensemble learning. Experiment of two CF methods integration on the data sets OTB demonstrates that the proposed method is effective and promising.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8100/_p
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@ARTICLE{e101-d_10_2547,
author={Wei WANG, Weiguang LI, Zhaoming CHEN, Mingquan SHI, },
journal={IEICE TRANSACTIONS on Information},
title={Twofold Correlation Filtering for Tracking Integration},
year={2018},
volume={E101-D},
number={10},
pages={2547-2550},
abstract={In general, effective integrating the advantages of different trackers can achieve unified performance promotion. In this work, we study the integration of multiple correlation filter (CF) trackers; propose a novel but simple tracking integration method that combines different trackers in filter level. Due to the variety of their correlation filter and features, there is no comparability between different CF tracking results for tracking integration. To tackle this, we propose twofold CF to unify these various response maps so that the results of different tracking algorithms can be compared, so as to boost the tracking performance like ensemble learning. Experiment of two CF methods integration on the data sets OTB demonstrates that the proposed method is effective and promising.},
keywords={},
doi={10.1587/transinf.2018EDL8100},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - Twofold Correlation Filtering for Tracking Integration
T2 - IEICE TRANSACTIONS on Information
SP - 2547
EP - 2550
AU - Wei WANG
AU - Weiguang LI
AU - Zhaoming CHEN
AU - Mingquan SHI
PY - 2018
DO - 10.1587/transinf.2018EDL8100
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2018
AB - In general, effective integrating the advantages of different trackers can achieve unified performance promotion. In this work, we study the integration of multiple correlation filter (CF) trackers; propose a novel but simple tracking integration method that combines different trackers in filter level. Due to the variety of their correlation filter and features, there is no comparability between different CF tracking results for tracking integration. To tackle this, we propose twofold CF to unify these various response maps so that the results of different tracking algorithms can be compared, so as to boost the tracking performance like ensemble learning. Experiment of two CF methods integration on the data sets OTB demonstrates that the proposed method is effective and promising.
ER -