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Twofold Correlation Filtering for Tracking Integration

Wei WANG, Weiguang LI, Zhaoming CHEN, Mingquan SHI

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Summary :

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.

Publication
IEICE TRANSACTIONS on Information Vol.E101-D No.10 pp.2547-2550
Publication Date
2018/10/01
Publicized
2018/07/10
Online ISSN
1745-1361
DOI
10.1587/transinf.2018EDL8100
Type of Manuscript
LETTER
Category
Image Recognition, Computer Vision

Authors

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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