Twofold Correlation Filtering for Tracking Integration

Wei WANG  Weiguang LI  Zhaoming CHEN  Mingquan SHI  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.10   pp.2547-2550
Publication Date: 2018/10/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDL8100
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
object tracking,  correlation filter,  end-to-end represent learning,  complementary features,  trackers integration,  

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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.