Siamese Network Algorithm Based on Multi-Scale Channel Attention Fusion and Multi-Scale Depth-Wise Cross Correlation

The research takes the feature extraction network and depth-wise cross correlation learning method of the Siamese network as the starting point. Firstly, the regression strategy of the proposed framework is anchor-free, and the residual network ResNet50 is chosen as the backbone network, and add the channel attention mechanism SENet. The SE-MSCAM multi-scale channel attention model is proposed to make up for the lack of local feature extraction ability of the feature extraction network on the basis of SENet. On this basis, the attention fusion module AFFN is added to enhance the soft selection of attention. Combined with the SE-MSCAM multi-scale attention model and the attention fusion module AFFN, the ResNet50-AFFN multi-scale channel attention fusion network is proposed. Secondly, regarding the limitation of single-scale learning of SiamRPN++ depth-wise cross correlation, the MS-DWXCorr multi-scale depth-wise cross correlation is proposed which increases the diversity of learning feature scales to improve the efficiency of tracking network similarity learning. The experimental results show that, on the VOT2018 benchmark, the EAO of our method outperforms 4.0% of the mainstream algorithm SiamCAR, the tracking accuracy is improved by 3.4% and the tracking speed of our method maintains 40 FPS; the tracking success rate is improved by 2.0% and the tracking accuracy rate is improved by 3.2% compared to the mainstream algorithm SiamCAR. It has higher accuracy and robustness in dealing with occlusion, deformation, illumination variation, deformation, and other scenarios of visual tracking, and has better tracking performance.