Siamese Network Visual Tracking Based on Asymmetric Convolution

PU Lei; WEI Zhenhua; HOU Zhiqiang; FENG Xinxi; HE Yujie

doi:10.11999/JEIT210472

Volume 44 Issue 8

Aug. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(8): 2957-2965

PU Lei, WEI Zhenhua, HOU Zhiqiang, FENG Xinxi, HE Yujie. Siamese Network Visual Tracking Based on Asymmetric Convolution[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2957-2965. doi: 10.11999/JEIT210472

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PU Lei, WEI Zhenhua, HOU Zhiqiang, FENG Xinxi, HE Yujie. Siamese Network Visual Tracking Based on Asymmetric Convolution[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2957-2965. doi: 10.11999/JEIT210472

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Siamese Network Visual Tracking Based on Asymmetric Convolution

doi: 10.11999/JEIT210472

1.
Combat Support College, Rocket Force University of Engineering, Xi’an 710025, China
2.
School of Computer Science and Technology, Xian University of Posts and Telecommunications, Xi’an 710121, China
3.
College of Artificial Intelligence, Yango University, Fuzhou 350015, China

Funds: The National Natural Science Foundation of China (62072370, 62006240)

Received Date: 2021-05-28
Rev Recd Date: 2022-05-30

Available Online: 2022-06-13

Publish Date: 2022-08-17

Abstract

Abstract

In order to solve the problem that the Siamese network can not express the rotating target, a Siamese network tracking algorithm based on asymmetric convolution is proposed. Firstly, asymmetric convolution kernels are constructed, which can be applied to existing networks. Then, under the framework of Siamese network, the convolution module of AlexNet is replaced, and the network is designed separately in the training and tracking stages. Finally, three asymmetric convolution kernels are added in parallel in the last layer of the network, and the maximum value is selected as the target position after the three response maps are weighted fused. The experimental results show that compared with SiamFC, the accuracy and success rate are improved by 8.7% and 4.5% on OTB2015 dataset, respectively.
- Visual tracking,
- Siamese network,
- Asymmetric convolution,
- Rotation

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References(27)

References

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