Limited by the imaging mechanism of visible light cameras, target tracking algorithms under visible light conditions typically perform poorly when lighting conditions are suboptimal or visibility is insufficient. RGBT (RGB-Thermal) target tracking effectively enhances perception capabilities by simultaneously utilizing color and texture information from RGB images and the temperature distribution characteristics from thermal infrared images, providing more accurate image understanding for intelligent transportation, autonomous driving, and security monitoring. In practical deployments of intelligent transportation, RGBT tracking can effectively address the problem of continuous target tracking in areas with significant lighting changes, such as tunnel entrances and underground garages. Additionally, in large hubs or complex interchange scenarios, the thermal infrared modality can assist in distinguishing overlapping vehicles and pedestrians, significantly reducing the risk of trajectory breaks caused by visual confusion. However, the RGBT target tracking field currently faces several challenges: first, how to improve feature identification in the representation of multimodal and multi-temporal information; second, exploring how to effectively enhance system robustness in cases where target appearance information is insufficient or unreliable. To address these issues, this paper proposes an RGBT target tracking algorithm based on Transformer motion cue enhancement (STARK-CMTM). In complex scenes where target objects are occluded, images are blurred, and there is confusion among multiple targets, the reliability of appearance information is significantly compromised. In such cases, solely relying on appearance information can no longer guarantee accuracy; therefore, this paper introduces camera and target motion information as important supplements. By exploring the mechanisms of corresponding motion models in depth, a method that effectively integrates target and camera motion cues into STARK is proposed, significantly improving tracker performance in complex environments.