关键词: 目标跟踪, 红外弱小目标, 孪生网络, transformer, 多特征提取

Abstract: Abstract: A method based on Siamese network and transformer is proposed to address the low accuracy problem of infrared dim small target tracking. First, a multi-feature extraction cascade module is 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) is introduced to perform cross-correlation operations on 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 are obtained through the response map upsampling network and bounding box prediction network to complete the tracking of the infrared dim small target. Test results on a dataset of 13,655 infrared images show that compared with the KeepTrack tracking method, the success rate is improved by 5.9 percentage points and the precision rate is improved by 1.8 percentage points. Compared with the TransT tracking method, the success rate is improved by 14.2 percentage points and the precision rate is improved by 14.6 percentage points. This proves that the proposed method has higher tracking accuracy for infrared dim small targets in complex backgrounds.

Key words: Target tracking, Infrared dim targets, Siamese networks, transformer, Multi-feature extraction