At present, most thermal infrared (TIR) tracking methods are based on correlation filters or RGB trackers for feature extraction. However, both of them are only suitable for RGB object tracking but not sensitive to the TIR object features, thus failing to be applied to the TIR object tracking. To this end, a TIR object tracker based on the global-aware siamese neural network was proposed in this paper. First, the features from the last three layers of the siamese neural network were fused to obtain new features. Second, the spatial-aware module composed of the spatial transformer network and channel attention was added to get the global effective information. Simultaneously, the self-attention mechanism was introduced to make the algorithm more focus on extracting the discriminant information of the objects. At last, the final response map was acquired by response fusion of the results. The experimental results on the TIR pedestrian tracking benchmark (PTB-TIR) show that the proposed algorithm can adapt to a variety of TIR environments while maintaining a high tracking speed (20.2 frame/s), achieving effective and stable real-time tracking of TIR objects.