Robust Visual Tracking Algorithm Based on Siamese Network with Dual Templates

Zhiqiang HOU; Lilin CHEN; Wangsheng YU; Sugang MA; Jiulun FAN

doi:10.11999/JEIT181018

Volume 41 Issue 9

Sep. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(9): 2247-2255

Zhiqiang HOU, Lilin CHEN, Wangsheng YU, Sugang MA, Jiulun FAN. Robust Visual Tracking Algorithm Based on Siamese Network with Dual Templates[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2247-2255. doi: 10.11999/JEIT181018

Citation:

Zhiqiang HOU, Lilin CHEN, Wangsheng YU, Sugang MA, Jiulun FAN. Robust Visual Tracking Algorithm Based on Siamese Network with Dual Templates[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2247-2255. doi: 10.11999/JEIT181018

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Robust Visual Tracking Algorithm Based on Siamese Network with Dual Templates

doi: 10.11999/JEIT181018

Zhiqiang HOU^{1, 3},
Lilin CHEN^{1, 3
,
,},
Wangsheng YU²,
Sugang MA^{1, 3},
Jiulun FAN¹

1.
Institute of Computer, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
Information and Navigation Institute, Air Force Engineering University, Xi’an 710077, China
3.
Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Funds: The National Natural Science Foundation of China (61473309, 61703423)

Received Date: 2018-11-06
Rev Recd Date: 2019-05-29

Available Online: 2019-06-12

Publish Date: 2019-09-10

Abstract

Abstract

In recent years, the Siamese networks has drawn great attention in visual tracking community due to its balanced accuracy and speed. However, most Siamese networks model are not updated, which causes tracking errors. In view of this deficiency, an algorithm based on the Siamese network with double templates is proposed. First, the base template R which is the initial frame target with stable response map score and the dynamic template T which is using the improved APCEs model update strategy to determine are kept. Then, the candidate targets region and the two template matching results are analyzed, meanwhile the result response maps are fused, which could ensure more accurate tracking results. The experimental results on the OTB2013 and OTB2015 datasets show that comparing with the 5 current mainstream tracking algorithms, the tracking accuracy and success rate of the proposed algorithm are superior. The proposed algorithm not only displays better tracking effects under the conditions of scale variation, in-plane rotation, out-of-plane rotation, occlusion, and illumination variation, but also achieves real-time tracking by a speed of 46 frames per second.
- Siamese network,
- Object tracking,
- Dual templates,
- Template update

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References(24)

References

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