Fully convolutional Siamese network (SiamFC) has demonstrated high performance in the visual tracking field, but the learned CNN features are redundant and not discriminative to separate the object from the background. To address the above problem, this paper proposes a dual attention module that is integrated into the Siamese network to select the features both in the spatial and channel domains. Especially, a non-local attention module is followed by the last layer of the network, and this benefit to obtain the self-attention feature map of the target from the spatial dimension. On the other hand, a channel attention module is proposed to adjust the importance of different channels’ features according to the corresponding responses generated by each channel feature and the target. Additionally, the GOT10k data set is employed to train our dual attention Siamese network (SiamDA) to improve the target representation ability, which enhances the discrimination of the model. Experimental results show that the proposed algorithm improves the accuracy by 7.6% and the success rate by 5.6% compared with the baseline tracker.

全卷积暹罗网络（SiamFC）在视觉跟踪领域表现出了出色的性能，但是学习到的CNN功能是多余的，不能区分对象与背景。为了解决上述问题，本文提出了一种双重注意模块，该模块集成到暹罗网络中以选择空间和通道域中的特征。特别是，非本地注意力模块后面是网络的最后一层，这从空间维度获得目标的自我注意力特征图很有用。另一方面，提出了一种频道关注模块，以根据每个频道特征和目标产生的相应响应来调整不同频道特征的重要性。此外，GOT10k数据集用于训练我们的双注意力暹罗网络（SiamDA），以提高目标表示能力，从而增强模型的辨别力。实验结果表明，与基线跟踪器相比，该算法的准确率提高了7.6％，成功率提高了5.6％。