A target tracking method based on positional perception is proposed to address the issue of low tracking accuracy caused by the absence of target detail information in thermal infrared images. First, semantic characteristics were extracted and thermal infrared objects were robustly characterized using the deep dilated residual network (D-ResNet). Second, a positional perception module was designed to efficiently detect the object position on the feature map and enhance the positioning accuracy of the algorithm. Third, the channel attention module was introduced to suppress interference information and filter feature map data in the channel domain. Then, the region proposal network was implemented to complete border regression and target categorization. Finally, RGBT234 thermal infrared sequences were used to adjust the network to successfully learn the thermal infrared object information. The proposed method is tested on VOT-TIR2019 and GTOT datasets and achieves accuracy of 75.3% and 91.4%, respectively, and a speed of 30 frame/s. Experimental results also demonstrate that the proposed method can realize high tracking accuracy in dealing with common difficulties, such as occlusion, analog interference, and scale change, effectively in the thermal infrared scene.