Aiming at the problems of poor robustness， low precision and success rate in the existing tiny object tracking algorithms， a tiny object tracking algorithm， SiamTrans， was proposed on the basis of Siamese network and Transformer. Firstly， a similarity response map calculation module was designed based on the Transformer mechanism. In the module， several layers of feature encoding-decoding structures were superimposed， and multi-head self-attention and multi-head cross-attention mechanisms were used to query template feature map information in feature maps of different levels of search regions， which avoided falling into local optimal solutions and obtained a high-quality similarity response map. Secondly， a Prediction Module （PM） based on Transformer mechanism was designed in the prediction subnetwork， and the self-attention mechanism was used to process redundant feature information in the prediction branch feature maps to improve the prediction precisions of different prediction branches. Experimental results on Small90 dataset show that， compared to the TransT （Transformer Tracking） algorithm， the tracking precision and tracking success rate of the proposed algorithm are 8.0 and 9.5 percentage points higher， respectively. It can be seen that the proposed algorithm has better tracking performance for tiny objects.