The field of thermal infrared target tracking is experiencing significant advancements with a focus on improving accuracy, robustness, and computational efficiency. Researchers are exploring innovative approaches to address common challenges such as low-resolution imagery, occlusion, background clutter, and target deformation. Notable developments include the integration of sparse learning, multi-task learning, and graph regularization to enhance tracking performance. Additionally, the use of super-resolution reconstruction and nonlocal similarity implicit neural representation is being investigated to improve feature extraction and target detection. These advancements have the potential to significantly enhance the reliability and accuracy of thermal infrared target tracking systems. Noteworthy papers include: RAMCT, which proposes a region-adaptive sparse correlation filter tracker with iterative Tikhonov regularization, achieving state-of-the-art performance on several benchmarks. FGSGT, which introduces a saliency-guided Siamese network tracker that captures fine-grained feature information, demonstrating superior accuracy and robustness on various benchmarks.