Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (8): 169-174.DOI: 10.3778/j.issn.1002-8331.2001-0330

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Siamese Network Tracking Algorithms for Hierarchical Fusion of Attention Mechanism

WANG Ling, WANG Jiapei, WANG Peng, SUN Shuangzi   

  1. College of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
  • Online:2021-04-15 Published:2021-04-23



  1. 长春理工大学 计算机科学技术学院,长春 130022


Based on the full-convolution Siamese network tracking algorithm SiamFC, this paper proposes a Siamese network target tracking algorithm fused attention mechanism. In the template branch, the neural network can learn the channel correlation and the spatial correlation of the template image through the attention mechanism fusion, thus increasing the foreground contribution, suppressing the background features, and improving the discrimination of network to positive samples features. Meanwhile, the VggNet-19 network is used to extract the shallow and deep features of the template image, the two features fuse adaptively. The experimental results on the datasets of OTB2015 and VOT2018 demonstrate that compared with SiamFC, the proposed algorithm can more effectively deal with the tracking problems, such as motion blur, target drift and background clutter, obtains higher accuracy and success rate.

Key words: object tracking, Siamese network, hierarchical fusion, attention mechanism



关键词: 目标跟踪, 孪生网络, 特征融合, 注意力机制