| Citation: | PU Lei, FENG Xinxi, HOU Zhiqiang, et al. Siamese network visual tracking algorithm based on cascaded attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(12): 2302-2310. doi: 10.13700/j.bh.1001-5965.2019.0601(in Chinese)
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Aimed at the problem that the Fully Convolutional Siamese Network (SiamFC) is easy to fail to track when it is disturbed by similar object or the target has large-scale appearance changes, this paper proposes a Siamese network visual tracking algorithm based on cascaded attention mechanism. First, the non-local attention module is added to the last layer of the network, and the self-attention feature map of the target area is obtained from the spatial dimension and is added with the last-layer feature. Then, considering the different responses of different channel features to different targets and scenes, the channel attention module is introduced to select the importance of feature channel. In order to further improve the robustness of tracking, it is weighted fused with SiamFC algorithm to obtain the final response map. Finally, the Siamese network model is proposed to jointly train on the GOT10k and VID data set to further improve the expression and discrimination of the model. Experimental results show that compared with SiamFC, the proposed algorithm improves the accuracy by 9.3% and the success rate by 5.4%.
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