Fast Robust Visual Tracking Based on Coding Transfer

XUE Mogen; LIU Wenzhuo; YUAN Guanglin; QIN Xiaoyan

doi:10.11999/JEIT160966

Volume 39 Issue 7

Jul. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(7): 1571-1577

XUE Mogen, LIU Wenzhuo, YUAN Guanglin, QIN Xiaoyan. Fast Robust Visual Tracking Based on Coding Transfer[J]. Journal of Electronics & Information Technology, 2017, 39(7): 1571-1577. doi: 10.11999/JEIT160966

Citation:

XUE Mogen, LIU Wenzhuo, YUAN Guanglin, QIN Xiaoyan. Fast Robust Visual Tracking Based on Coding Transfer[J]. Journal of Electronics & Information Technology, 2017, 39(7): 1571-1577. doi: 10.11999/JEIT160966

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Fast Robust Visual Tracking Based on Coding Transfer

doi: 10.11999/JEIT160966

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

Funds:

The National Natural Science Foundation of China (61175035, 61379105)

Received Date: 2016-09-26
Rev Recd Date: 2017-02-08
Publish Date: 2017-07-19

Abstract

Abstract

The sparsity constraint of the L1 trackers representation model makes it have good robustness towards partial occlusion. However, the tracking speed of the L1 tracker is slow. To solve this study, this paper proposes a coding transfer method for visual tracking. By making use of the low-resolution dictionary to calculate coefficients of the candidate targets and the high-resolution dictionary to construct the observation likelihood model, the method reduces calculation amount effectively in the process of tracking. In order to improve the precision of coding transfer and the ability of the dictionary to overcome the background clutters, this study proposes an online robust discrimination joint dictionary learning model to update the dictionaries. The experimental results demonstrate that the proposed method has good robustness and superior tracking speed.
- L1 tracker,
- Coding transfer,
- Dictionary learning,
- Particle filter

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References(20)

References

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