Feature Adaptive Correlation Tracking

Yulong XU  Yang LI  Jiabao WANG  Zhuang MIAO  Hang LI  Yafei ZHANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.3   pp.594-597
Publication Date: 2017/03/01
Publicized: 2016/11/28
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDL8164
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
visual tracking,  correlation filter,  feature selection,  convolutional neural networks,  scale estimation,  

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Feature extractor plays an important role in visual tracking, but most state-of-the-art methods employ the same feature representation in all scenes. Taking into account the diverseness, a tracker should choose different features according to the videos. In this work, we propose a novel feature adaptive correlation tracker, which decomposes the tracking task into translation and scale estimation. According to the luminance of the target, our approach automatically selects either hierarchical convolutional features or histogram of oriented gradient features in translation for varied scenarios. Furthermore, we employ a discriminative correlation filter to handle scale variations. Extensive experiments are performed on a large-scale benchmark challenging dataset. And the results show that the proposed algorithm outperforms state-of-the-art trackers in accuracy and robustness.