Multi-scale single object tracking based on the attention mechanism

In the process of single object tracking, due to problems of occlusion, disappearance and similar target interference, the tracking accuracy of the algorithm will be reduced. In order to solve these problems, a multi-scale single target tracking algorism based on the attention mechanism is proposed which uses asymmetric convolution to extract the multi-scale feature while reducing the parameters. It combines local features and global features to improve tracking robustness. The online update algorithm based on the attention mechanism is used which combines the response diagram and attention diagram to calculate a score which is used to weed out frames without targets. The attention mechanism strengthens the ability to distinguish the target and background, makes the network quickly adapt to the changes of the appearance of the targets, and improves the tracking performance of the algorithm. The algorithm is tested on OTB-100 datasets with other advanced tracking methods. Compared to the ATOM, the accuracy and success rate of our method are improved by 0.9% and 0.8% respectively and it is easier to retrieve the target after it is lost. © 2021, The Editorial Board of Journal of Xidian University. All right reserved.