A method based on Siamese network and Transformer was proposed to address the low accuracy problem of infrared dim small target tracking. First， a multi-feature extraction cascading moduling was constructed to separately extract the deep features of the infrared dim small target template frame and the search frame， and concatenate them with their corresponding HOG features at the dimension level. Second， a multi-head attention mechanism Transformer was introduced to perform cross-correlation operations between the template feature map and the search feature map， generating a response map. Finally， the target’s center position in the image and the regression bounding box were obtained through the response map upsampling network and bounding box prediction network to complete the tracking of the infrared dim small targets. Test results on a dataset of 13 655 infrared images show that compared with KeepTrack tracking method， the success rate is improved by 5.9 percentage points and the precision is improved by 1.8 percentage points； compared with TransT （Transformer Tracking） method， the success rate is improved by 14.2 percentage points and the precision is improved by 14.6 percentage points. The proposed method is proved to be more accurate in tracking infrared dim small targets in complex backgrounds.