Robust Visual Tracking Based on Online Discrimination Dictionary Learning

Xue Mo-gen; Zhu Hong; Yuan Guang-lin

doi:10.11999/JEIT141325

Volume 37 Issue 7

Jul. 2015

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(7): 1654-1659

Xue Mo-gen, Zhu Hong, Yuan Guang-lin. Robust Visual Tracking Based on Online Discrimination Dictionary Learning[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1654-1659. doi: 10.11999/JEIT141325

Citation:

Xue Mo-gen, Zhu Hong, Yuan Guang-lin. Robust Visual Tracking Based on Online Discrimination Dictionary Learning[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1654-1659. doi: 10.11999/JEIT141325

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Robust Visual Tracking Based on Online Discrimination Dictionary Learning

doi: 10.11999/JEIT141325

1.
(Anhui Province Key Laboratory of Polarization Imaging Detection Technology, Army Officer Academy of PLA, Hefei 230031, China)
2.
(Anhui Province Key Laboratory of Polarization Imaging Detection Technology, Army Officer Academy of PLA, Hefei 230031, China)

Received Date: 2014-10-20
Rev Recd Date: 2015-02-09
Publish Date: 2015-07-19

Abstract

Abstract

The existing subspace tracking methods have well solved appearance changes and occlusions. However, they are weakly robust to complex background. To deal with this problem, firstly, this paper proposes an online discrimination dictionary learning model based on the Fisher criterion. The online discrimination dictionary learning algorithm for template updating in visual tracking is designed by using the block coordinate descent and replacing operations. Secondly, the distance between the target candidate coding coefficient and the mean of target samples coding coefficients is defined as the coefficient error. The robust visual tracking is achieved by taking the combination of the reconstruction error and the coefficient error as observation likelihood in particle filter framework. The experimental results show that the proposed method has better robustness and accuracy than the state-of-the-art trackers.
- Visual tracking,
- Template updating,
- Dictionary learning,
- Observation likelihood

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References(15)

References

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