Abstract: With the development of artificial intelligence,deep learning has attracted extensive attention in the research of computer vision.In the field of single target tracking,the single target tracking algorithm based on deep learning has been studied.The algorithm complexity of deep learning algorithm is relatively high.The complete segmentation of target classification and target state estimation is conducive to the in-depth discussion of each task.However,the current single target tracking algorithm can not deal with the complex tracking environment well.When the model encounters the complex tracking environment,it often tracks a certain area of the background or tracks the surrounding similar targets.In order to solve the above problems.In this paper,a method is proposed:different attention mechanisms are added to the task of target classification and target state estimation respectively,so that the model can better deal with background confusion and occlusion of similar targets.In order to verify the effectiveness of the above methods,this paper has done a lot of comparative experiments on multiple datasets,and compared with the previous single target tracking algorithm based on deep learning.The proposed algorithm improves 3.1% in the EAO index and 2.3% in the Robustness index.It shows the effectiveness and progressiveness of this method.

Key words: Computer vision, Single target tracking, Attention mechanism, Weight distribution, anomaly detection